Objectives This study aimed to develop and preliminarily assess the quality of a Mindfulness Breast Care(MBC)App to reduce body image distress and stigma among breast cancer survivors(BCSs).Methods The development pro...Objectives This study aimed to develop and preliminarily assess the quality of a Mindfulness Breast Care(MBC)App to reduce body image distress and stigma among breast cancer survivors(BCSs).Methods The development process of the MBC App involved:1)establishing a research group;2)determining of the content of the MBC App based on Mindfulness-Based Cognitive Therapy and 3)technical exploitation and maintenance.A mixed-methods study was conducted.We selected ten BCSs by a convenience sampling method.After using the APP for three months,five assessed the quality using the Mobile App Rating Scale:User Version(uMARS)and another five were interviewed for process evaluation.Results The MBC App was developed with three modules:1)Library to provide health education information on body image,stigma,mindfulness,recovery and etc;2)Mindfulness Yoga to offer 12 Hatha yoga videos for daily practice;and 3)Mindfulness Practices to have 12 sessions of mindfulness videoconferences.Based on the uMARS data,the MBC App received high ratings for functionality(4.10±0.34),aesthetics(3.93±0.55),information quality(4.10±0.72),and perceived impact(4.03±0.96),as well as moderate ratings for engagement(3.72±0.94)and subjective quality(3.87±0.77).Participants indicated that the MBC App provided reliable knowledge,information,and emotional support.Recommendations from participants included categorizing knowledge in the Library Module,recording videoconferences of mindfulness practice,and adding discussion sessions in the videoconference.Afterward,we optimized the MBC App to enhance the user experience accordingly.Conclusions The MBC App offers online mindfulness interventions specifically for BCSs in China.The preliminary quality assessment indicates that the MBC App may be a promising tool for delivering mindfulness interventions to BCSs.展开更多
Previous research utilizing Cartoon Generative Adversarial Network(CartoonGAN)has encountered limitations in managing intricate outlines and accurately representing lighting effects,particularly in complex scenes requ...Previous research utilizing Cartoon Generative Adversarial Network(CartoonGAN)has encountered limitations in managing intricate outlines and accurately representing lighting effects,particularly in complex scenes requiring detailed shading and contrast.This paper presents a novel Enhanced Pixel Integration(EPI)technique designed to improve the visual quality of images generated by CartoonGAN.Rather than modifying the core model,the EPI approach employs post-processing adjustments that enhance images without significant computational overhead.In this method,images produced by CartoonGAN are converted from Red-Green-Blue(RGB)to Hue-Saturation-Value(HSV)format,allowing for precise adjustments in hue,saturation,and brightness,thereby improving color fidelity.Specific correction values are applied to fine-tune colors,ensuring they closely match the original input while maintaining the characteristic,stylized effect of CartoonGAN.The corrected images are blended with the originals to retain aesthetic appeal and visual distinctiveness,resulting in improved color accuracy and overall coherence.Experimental results demonstrate that EPI significantly increases similarity to original input images compared to the standard CartoonGAN model,achieving a 40.14%enhancement in visual similarity in Learned Perceptual Image Patch Similarity(LPIPS),a 30.21%improvement in structural consistency in Structural Similarity Index Measure(SSIM),and an 11.81%reduction in pixel-level error in Mean Squared Error(MSE).By addressing limitations present in the traditional CartoonGAN pipeline,EPI offers practical enhancements for creative applications,particularly within media and design fields where visual fidelity and artistic style preservation are critical.These improvements align with the goals of Fog and Edge Computing,which also seek to enhance processing efficiency and application performance in sensitive industries such as healthcare,logistics,and education.This research not only resolves key deficiencies in existing CartoonGAN models but also expands its potential applications in image-based content creation,bridging gaps between technical constraints and creative demands.Future studies may explore the adaptability of EPI across various datasets and artistic styles,potentially broadening its impact on visual transformation tasks.展开更多
Recent deep neural network(DNN)based blind image quality assessment(BIQA)approaches take mean opinion score(MOS)as ground-truth labels,which would lead to cross-datasets biases and limited generalization ability of th...Recent deep neural network(DNN)based blind image quality assessment(BIQA)approaches take mean opinion score(MOS)as ground-truth labels,which would lead to cross-datasets biases and limited generalization ability of the DNN-based BIQA model.This work validates the natural instability of MOS through investigating the neuropsychological characteristics inside the human visual system during quality perception.By combining persistent homology analysis with electroencephalogram(EEG),the physiologically meaningful features of the brain responses to different distortion levels are extracted.The physiological features indicate that although volunteers view exactly the same image content,their EEG features are quite varied.Based on the physiological results,we advocate treating MOS as noisy labels and optimizing the DNN based BIQA model with earlystop strategies.Experimental results on both innerdataset and cross-dataset demonstrate the superiority of our optimization approach in terms of generalization ability.展开更多
Neutron radiographic images(NRIs)typically suffer from multiple distortions,including various types of noise,geometric unsharpness,and white spots.Image quality assessment(IQA)can guide on-site image screening and eve...Neutron radiographic images(NRIs)typically suffer from multiple distortions,including various types of noise,geometric unsharpness,and white spots.Image quality assessment(IQA)can guide on-site image screening and even provide metrics for subsequent image processing.However,existing IQA methods for NRIs cannot effectively evaluate the quality of real NRIs with a specific distortion of white spots,limiting their practical application.In this paper,a novel no-reference IQA method is proposed to comprehensively evaluate the quality of real NRIs with multiple distortions.First,we construct large-scale NRI datasets with more than 20,000 images,including high-quality original NRIs and synthetic NRIs with various distortions.Next,an image quality calibration method based on visual salience and a local quality map is introduced to label the NRI dataset with quality scores.Finally,a lightweight convolutional neural network(CNN)model is designed to learn the abstract relationship between the NRIs and quality scores using the constructed NRI training dataset.Extensive experimental results demonstrate that the proposed method exhibits good consistency with human visual perception when evaluating both real NRIs and processed NRIs using enhancement and restoration algorithms,highlighting its application potential.展开更多
Objective:In the Radiology Department of Mzuzu Central Hospital(MCH),daily training for radiographers now includes content on Computed Tomography(CT)image quality control and equipment maintenance to ensure the normal...Objective:In the Radiology Department of Mzuzu Central Hospital(MCH),daily training for radiographers now includes content on Computed Tomography(CT)image quality control and equipment maintenance to ensure the normal,continuous,and stable operation of the 16-slice spiral CT scanner.Methods:Through comprehensive analysis of relevant equipment,we have identified key parameters that significantly impact CT image quality.Innovative optimization strategies and solutions targeting these parameters have been developed and integrated into daily training programs.Furthermore,starting from an examination of prevalent failure modes observed in CT equipment,we delve into essential maintenance and preservation techniques that CT technologists must master to ensure optimal system performance.Results:(1)Crucial factors affecting CT image quality include artifacts,noise,partial volume effects,and surrounding gap phenomena,alongside spatial and density resolutions,CT dose,reconstruction algorithms,and human factors during the scanning process.In the daily training for radiographers,emphasis is placed on strictly implementing image quality control measures at every stage of the CT scanning process and skillfully applying advanced scanning and image processing techniques.By doing so,we can provide clinicians with accurate and reliable imaging references for diagnosis and treatment.(2)Strategies for CT equipment maintenance:①Environmental inspection of the CT room to ensure cleanliness and hygiene.②Rational and accurate operation,including calibration software proficiency.③Regular maintenance and servicing for minimizing machine downtime.④Maintenance of the CT X-ray tube.CT technicians can become proficient in equipment maintenance and upkeep techniques through training,which can significantly extend the service life of CT systems and reduce the occurrence of malfunctions.Conclusion:Through the regular implementation of rigorous CT image quality control training for radiology technicians,coupled with diligent and proactive CT equipment maintenance,we have observed profound and beneficial impacts on improving image quality.The accuracy and fidelity of radiological data ultimately leads to more accurate diagnoses and effective treatments.展开更多
This paper proposes a novel method for the automatic diagnosis of keratitis using feature vector quantization and self-attention mechanisms(ADK_FVQSAM).First,high-level features are extracted using the DenseNet121 bac...This paper proposes a novel method for the automatic diagnosis of keratitis using feature vector quantization and self-attention mechanisms(ADK_FVQSAM).First,high-level features are extracted using the DenseNet121 backbone network,followed by adaptive average pooling to scale the features to a fixed length.Subsequently,product quantization with residuals(PQR)is applied to convert continuous feature vectors into discrete features representations,preserving essential information insensitive to image quality variations.The quantized and original features are concatenated and fed into a self-attention mechanism to capture keratitis-related features.Finally,these enhanced features are classified through a fully connected layer.Experiments on clinical low-quality(LQ)images show that ADK_FVQSAM achieves accuracies of 87.7%,81.9%,and 89.3% for keratitis,other corneal abnormalities,and normal corneas,respectively.Compared to DenseNet121,Swin transformer,and InceptionResNet,ADK_FVQSAM improves average accuracy by 3.1%,11.3%,and 15.3%,respectively.These results demonstrate that ADK_FVQSAM significantly enhances the recognition performance of keratitis based on LQ slit-lamp images,offering a practical approach for clinical application.展开更多
Most blind image quality assessment(BIQA)methods require a large amount of time to collect human opinion scores as training labels,which limits their usability in practice.Thus,we present an opinion-unaware BIQA metho...Most blind image quality assessment(BIQA)methods require a large amount of time to collect human opinion scores as training labels,which limits their usability in practice.Thus,we present an opinion-unaware BIQA method based on deep reinforcement learning which is trained without subjective scores,named DRL-IQA.Inspired by the human visual perception process,our model is formulated as a quality reinforced agent,which consists of the dynamic distortion generation part and the quality perception part.By considering the image distortion degradation process as a sequential decision-making process,the dynamic distortion generation part can develop a strategy to add as many different distortions as possible to an image,which enriches the distortion space to alleviate overfitting.A reward function calculated from quality degradation after adding distortion is utilized to continuously optimize the strategy.Furthermore,the quality perception part can extract rich quality features from the quality degradation process without using subjective scores,and accurately predict the state values that represent the image quality.Experimental results reveal that our method achieves competitive quality prediction performance compared to other state-of-the-art BIQA methods.展开更多
Indirect X-ray modulation imaging has been adopted in a number of solar missions and provided reconstructed X-ray images of solar flares that are of great scientific importance.However,the assessment of the image qual...Indirect X-ray modulation imaging has been adopted in a number of solar missions and provided reconstructed X-ray images of solar flares that are of great scientific importance.However,the assessment of the image quality of the reconstruction is still difficult,which is particularly useful for scheme design of X-ray imaging systems,testing and improvement of imaging algorithms,and scientific research of X-ray sources.Currently,there is no specified method to quantitatively evaluate the quality of X-ray image reconstruction and the point-spread function(PSF)of an X-ray imager.In this paper,we propose percentage proximity degree(PPD)by considering the imaging characteristics of X-ray image reconstruction and in particular,sidelobes and their effects on imaging quality.After testing a variety of imaging quality assessments in six aspects,we utilized the technique for order preference by similarity to ideal solution to the indices that meet the requirements.Then we develop the final quality index for X-ray image reconstruction,QuIX,which consists of the selected indices and the new PPD.QuIX performs well in a series of tests,including assessment of instrument PSF and simulation tests under different grid configurations,as well as imaging tests with RHESSI data.It is also a useful tool for testing of imaging algorithms,and determination of imaging parameters for both RHESSI and ASO-S/Hard X-ray Imager,such as field of view,beam width factor,and detector selection.展开更多
The in-flight calibration and performance of the Solar Disk Imager(SDI),which is a pivotal instrument of the LyαSolar Telescope onboard the Advanced Space-based Solar Observatory mission,suggested a much lower spatia...The in-flight calibration and performance of the Solar Disk Imager(SDI),which is a pivotal instrument of the LyαSolar Telescope onboard the Advanced Space-based Solar Observatory mission,suggested a much lower spatial resolution than expected.In this paper,we developed the SDI point-spread function(PSF)and Image Bivariate Optimization Algorithm(SPIBOA)to improve the quality of SDI images.The bivariate optimization method smartly combines deep learning with optical system modeling.Despite the lack of information about the real image taken by SDI and the optical system function,this algorithm effectively estimates the PSF of the SDI imaging system directly from a large sample of observational data.We use the estimated PSF to conduct deconvolution correction to observed SDI images,and the resulting images show that the spatial resolution after correction has increased by a factor of more than three with respect to the observed ones.Meanwhile,our method also significantly reduces the inherent noise in the observed SDI images.The SPIBOA has now been successfully integrated into the routine SDI data processing,providing important support for the scientific studies based on the data.The development and application of SPIBOA also paves new ways to identify astronomical telescope systems and enhance observational image quality.Some essential factors and precautions in applying the SPIBOA method are also discussed.展开更多
In Hyperspectral Imaging(HSI),the detrimental influence of noise and distortions on data quality is profound,which has severely affected the following-on analytics and decisionmaking such as land mapping.This study pr...In Hyperspectral Imaging(HSI),the detrimental influence of noise and distortions on data quality is profound,which has severely affected the following-on analytics and decisionmaking such as land mapping.This study presents an innovative framework for assessing HSI band quality and reconstructing the low-quality bands,based on the Prophet model.By introducing a comprehensive quality metric to start,the authors approach factors in both spatial and spectral characteristics across local and global scales.This metric effectively captures the intricate noise and distortions inherent in the HSI data.Subsequently,the authors employ the Prophet model to forecast information within the low-quality bands,leveraging insights from neighbouring high-quality bands.To validate the effectiveness of the authors’proposed model,extensive experiments on three publicly available uncorrected datasets are conducted.In a head-to-head comparison,the framework against six state-ofthe-art band reconstruction algorithms including three spectral methods,two spatialspectral methods and one deep learning method is benchmarked.The authors’experiments also delve into strategies for band selection based on quality metrics and the quality evaluation of the reconstructed bands.In addition,the authors assess the classification accuracy utilising these reconstructed bands.In various experiments,the results consistently affirm the efficacy of the authors’method in HSI quality assessment and band reconstruction.Notably,the authors’approach obviates the need for manually prefiltering of noisy bands.This comprehensive framework holds promise in addressing HSI data quality concerns whilst enhancing the overall utility of HSI.展开更多
Air quality estimation assesses the pollution level in the air,supports public health warnings,and is a valuable tool in environmental management.Although air sensors have proven helpful in this task,sensors are often...Air quality estimation assesses the pollution level in the air,supports public health warnings,and is a valuable tool in environmental management.Although air sensors have proven helpful in this task,sensors are often expensive and difficult to install,while cameras are becoming more popular and accessible,from which images can be collected as data for deep learning models to solve the above task.This leads to another problem:several labeled images are needed to achieve high accuracy when deep-learningmodels predict air quality.In this research,we have threemain contributions:(1)Collect and publish an air quality estimation dataset,namely PTIT_AQED,including environmental image data and air quality;(2)Propose a deep learning model to predict air quality with few data,called PTIT_FAQE(PTIT Few-shot air quality estimation).We build PTIT_FAQE based on EfficientNet-a CNN architecture that ensures high performance in deep learning applications and Few-shot Learning with Prototypical Networks.This helps the model use only a fewtraining data but still achieve high accuracy in air quality estimation.And(3)conduct experiments to prove the superiority of PTIT_FAQE compared to other studies on both PTIT_AQED and APIN datasets.The results show that our model achieves an accuracy of 0.9278 and an F1-Score of 0.9139 on the PTIT_AQED dataset and an accuracy of 0.9467 and an F1-Score of 0.9371 on the APIN dataset,which demonstrate a significant performance improvement compared to previous studies.We also conduct detailed experiments to evaluate the impact of each component on model performance.展开更多
Background:Cyperi Rhizoma,derived from Cyperus rotundus L.,is a widely used medicinal herb in traditional Chinese medicine(TCM),with Shandong Province recognized as its geo-authentic habitat.However,the quality of Cyp...Background:Cyperi Rhizoma,derived from Cyperus rotundus L.,is a widely used medicinal herb in traditional Chinese medicine(TCM),with Shandong Province recognized as its geo-authentic habitat.However,the quality of Cyperi Rhizoma varies significantly across different regions,potentially influencing its therapeutic efficacy.This study investigates the influence of geographic origin on the chemical composition and overall quality of Cyperi Rhizoma.Methods:A comprehensive approach,including traditional quality assessment,GC-MS(g as c hromatography-m ass s pectrometry),RP-HPLC(r everse p hase h igh-p erformance l iquid c hromatography),and FT-IR(f ourier t ransform i nfrared s pectroscopy)techniques,was employed to analyze Cyperi Rhizoma samples from Shandong Province.These methods examined the physical appearance,chemical profile,and content variations,particularly focusing onα-cyperone.Results:Traditional quality assessments revealed noticeable differences in the external characteristics of the samples.GC-MS analysis identified a variety of unique chemical constituents,while RP-HPLC and FT-IR showed significant variations inα-cyperone content,with higher levels found in Shandong samples.Conclusion:These results demonstrate that geographic origin is a critical determinant of Cyperi Rhizoma quality,with Shandong specimens exhibiting superiorα-cyperone levels and characteristic phytochemical profiles.This validates the geo-authenticity concept in TCM and provides actionable data for developing evidence-based quality standards,suggesting that provenance should be prioritized in medicinal material selection and pharmacopeial specifications.展开更多
Digital watermarking technology plays an important role in detecting malicious tampering and protecting image copyright.However,in practical applications,this technology faces various problems such as severe image dis...Digital watermarking technology plays an important role in detecting malicious tampering and protecting image copyright.However,in practical applications,this technology faces various problems such as severe image distortion,inaccurate localization of the tampered regions,and difficulty in recovering content.Given these shortcomings,a fragile image watermarking algorithm for tampering blind-detection and content self-recovery is proposed.The multi-feature watermarking authentication code(AC)is constructed using texture feature of local binary patterns(LBP),direct coefficient of discrete cosine transform(DCT)and contrast feature of gray level co-occurrence matrix(GLCM)for detecting the tampered region,and the recovery code(RC)is designed according to the average grayscale value of pixels in image blocks for recovering the tampered content.Optimal pixel adjustment process(OPAP)and least significant bit(LSB)algorithms are used to embed the recovery code and authentication code into the image in a staggered manner.When detecting the integrity of the image,the authentication code comparison method and threshold judgment method are used to perform two rounds of tampering detection on the image and blindly recover the tampered content.Experimental results show that this algorithm has good transparency,strong and blind detection,and self-recovery performance against four types of malicious attacks and some conventional signal processing operations.When resisting copy-paste,text addition,cropping and vector quantization under the tampering rate(TR)10%,the average tampering detection rate is up to 94.09%,and the peak signal-to-noise ratio(PSNR)of the watermarked image and the recovered image are both greater than 41.47 and 40.31 dB,which demonstrates its excellent advantages compared with other related algorithms in recent years.展开更多
A large-scale view of the magnetospheric cusp is expected to be obtained by the Soft X-ray Imager(SXI)onboard the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE).However,it is challenging to trace the three-d...A large-scale view of the magnetospheric cusp is expected to be obtained by the Soft X-ray Imager(SXI)onboard the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE).However,it is challenging to trace the three-dimensional cusp boundary from a two-dimensional X-ray image because the detected X-ray signals will be integrated along the line of sight.In this work,a global magnetohydrodynamic code was used to simulate the X-ray images and photon count images,assuming an interplanetary magnetic field with a pure Bz component.The assumption of an elliptic cusp boundary at a given altitude was used to trace the equatorward and poleward boundaries of the cusp from a simulated X-ray image.The average discrepancy was less than 0.1 RE.To reduce the influence of instrument effects and cosmic X-ray backgrounds,image denoising was considered before applying the method above to SXI photon count images.The cusp boundaries were reasonably reconstructed from the noisy X-ray image.展开更多
Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approach...Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.展开更多
AIM:To assess the visual acuity and visual quality of cataract patients who received femtosecond laser-assisted cataract surgery(FLACS)and multifocal intraocular lens(MIOL)implantation with an improved polishing techn...AIM:To assess the visual acuity and visual quality of cataract patients who received femtosecond laser-assisted cataract surgery(FLACS)and multifocal intraocular lens(MIOL)implantation with an improved polishing technique during a 1-year follow-up period.METHODS:This study included 74 eyes from 37 patients,comprising 17 males and 20 females,with a mean age of 51.74±7.80 years.Using a coin toss method,one eye per patient was randomly selected for improved anterior capsular polishing,while the other eye received standard irrigation/aspiration(I/A)polishing.The polishing group consisted of 37 eyes,including 21 right and 16 left eyes,while the control group comprised the contralateral fellow eyes of the same individuals in the polishing group.Visual acuity and quality of the patients were evaluated before surgery and at 1 wk,1,6,and 12 mo after surgery.The OPD-Scan III was utilized to assess high-order aberrations,while the optical quality analysis system(OQAS)was employed to evaluate modulation transfer function(MTF cutoff),Strehl ratio(SR),and objective scatter index(OSI)for the purpose of visual quality assessment.Paired t-tests and repeated measures analysis of variance(ANOVA)were utilized to compare the results,and the SNK-q post hoc test was applied to identify significant differences.RESULTS:The polishing group’s uncorrected distant visual acuity(UDVA)and uncorrected near visual acuity(UNVA)significantly improved 1-week post-surgery(all P<0.05).At 12-months,UDVA,UNVA,and corrected distant visual acuity(CDVA)were better than the control group(P<0.05).The MTF cutoff,SR,OSI,and high-order aberrations in the polishing group also showed significant improvements compared to the control group at 12 mo(P<0.05).CONCLUSION:The improved capsular polishing method has been demonstrated to effectively maintain visual acuity and visual quality in patients with MIOL after FLACS within 1 a.展开更多
BACKGROUND Dry eye disease(DED)is a multifactorial ocular surface disorder with rising prevalence.It is closely related to systemic health and psychological factors,such as sleep and mood disorders,which significantly...BACKGROUND Dry eye disease(DED)is a multifactorial ocular surface disorder with rising prevalence.It is closely related to systemic health and psychological factors,such as sleep and mood disorders,which significantly impact the quality of life of patients.AIM To explore the correlations between ocular surface function,sleep quality,and anxiety/depression in patients with DED.METHODS This was a cross-sectional investigative study that included 358 patients with DED between January 2022 and January 2025.Ocular surface was assessed using the ocular surface disease index(OSDI),tear film break-up time,fluorescein staining score,and Schirmer I test.The Pittsburgh Sleep Quality Index(PSQI),Self-Rating Anxiety Scale(SAS),and Self-Rating Depression Scale(SDS)were used to evaluate sleep quality and anxiety/depression levels.Correlation and linear regression analyses were used to explore the relationships.RESULTS The mean PSQI score of the patients was 9.94±2.18;the mean SAS score was 47.30±4.90,and the mean SDS score was 50.08±5.52.These suggested a prevalence of sleep and psychological abnormalities.There was a significant correlation between the indicators of ocular surface function(OSDI,tear film break-up time,fluorescein staining,and Schirmer I test)and PSQI,SAS,and SDS scores(P<0.05).Moreover,multiple regression revealed that age≥50 years(β=1.55,P=0.029),PSQI scores(β=0.58,P<0.001),SAS scores(β=0.17,P=0.017),and SDS scores(β=0.15,P=0.019)were independent predictors of the OSDI scores.CONCLUSION Ocular surface function in patients with DED is closely related to sleep quality and anxiety/depression,emphasizing the need for holistic clinical management.展开更多
High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes an...High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.展开更多
BACKGROUND Approximately 30%of patients with head and neck cancer experience adverse effects caused by anxiety and depression.Considering the high prevalence,implementing customized interventions to ease adverse emoti...BACKGROUND Approximately 30%of patients with head and neck cancer experience adverse effects caused by anxiety and depression.Considering the high prevalence,implementing customized interventions to ease adverse emotional states is imperative.AIM To evaluate the efficacy of cognitive behavioral therapy(CBT)-based psychological interventions in improving the psychological well-being and quality of life(QoL)of patients with laryngeal carcinoma.METHODS This study enrolled 120 patients admitted from February 2022 to February 2024.The control group,comprising 50 participants,received standard supportive psychological care,while the research group,consisting 70 participants,underwent CBT-based interventions.Several clinical outcomes were systematically assessed that included postoperative recovery metrics(duration of tracheostomy and nasogastric tube dependence and length of hospitalization),psychological status(Self-Rating Anxiety Scale and Self-Rating Depression Scale),nutritional markers(serum albumin and hemoglobin levels),sleep quality(Self-Rating Scale of Sleep and Athens Insomnia Scale),and QoL(Functional Assessment of Cancer Therapy-Head and Neck).RESULTS The results demonstrated that the research group experienced superior outcomes,with significantly reduced durations of tracheostomy and nasogastric tube dependence,as well as shorter hospital stays,compared with the control group.Additionally,the research group exhibited markedly lower post-intervention Self-Rating Anxiety Scale,Self-Rating Depression Scale,Self-Rating Scale of Sleep,and Athens Insomnia Scale scores,along with minimal but higher change in serum albumin and hemoglobin levels compared with the control group.All five domains of Functional Assessment of Cancer Therapy-Head and Neck showed notable improvements in the research group,exceeding those observed in the control group.CONCLUSION CBT-based psychological support positively affects the mental well-being and QoL of patients with laryngeal carcinoma,highlighting its potential for broader clinical application.展开更多
Chinese President Xi Jinping has guided China through a year of resilient growth via forward-looking reforms and innovation-driven transformation that is shaping the nation’s economic trajectory for 2026 and beyond.
基金supported by the National Natural Science Foundation of China(No.71974162 and No.7231101009).
文摘Objectives This study aimed to develop and preliminarily assess the quality of a Mindfulness Breast Care(MBC)App to reduce body image distress and stigma among breast cancer survivors(BCSs).Methods The development process of the MBC App involved:1)establishing a research group;2)determining of the content of the MBC App based on Mindfulness-Based Cognitive Therapy and 3)technical exploitation and maintenance.A mixed-methods study was conducted.We selected ten BCSs by a convenience sampling method.After using the APP for three months,five assessed the quality using the Mobile App Rating Scale:User Version(uMARS)and another five were interviewed for process evaluation.Results The MBC App was developed with three modules:1)Library to provide health education information on body image,stigma,mindfulness,recovery and etc;2)Mindfulness Yoga to offer 12 Hatha yoga videos for daily practice;and 3)Mindfulness Practices to have 12 sessions of mindfulness videoconferences.Based on the uMARS data,the MBC App received high ratings for functionality(4.10±0.34),aesthetics(3.93±0.55),information quality(4.10±0.72),and perceived impact(4.03±0.96),as well as moderate ratings for engagement(3.72±0.94)and subjective quality(3.87±0.77).Participants indicated that the MBC App provided reliable knowledge,information,and emotional support.Recommendations from participants included categorizing knowledge in the Library Module,recording videoconferences of mindfulness practice,and adding discussion sessions in the videoconference.Afterward,we optimized the MBC App to enhance the user experience accordingly.Conclusions The MBC App offers online mindfulness interventions specifically for BCSs in China.The preliminary quality assessment indicates that the MBC App may be a promising tool for delivering mindfulness interventions to BCSs.
基金supported by the National Research Foundation of Korea(NRF)under Grant RS-2022-NR-069955(2022R1A2C1092178).
文摘Previous research utilizing Cartoon Generative Adversarial Network(CartoonGAN)has encountered limitations in managing intricate outlines and accurately representing lighting effects,particularly in complex scenes requiring detailed shading and contrast.This paper presents a novel Enhanced Pixel Integration(EPI)technique designed to improve the visual quality of images generated by CartoonGAN.Rather than modifying the core model,the EPI approach employs post-processing adjustments that enhance images without significant computational overhead.In this method,images produced by CartoonGAN are converted from Red-Green-Blue(RGB)to Hue-Saturation-Value(HSV)format,allowing for precise adjustments in hue,saturation,and brightness,thereby improving color fidelity.Specific correction values are applied to fine-tune colors,ensuring they closely match the original input while maintaining the characteristic,stylized effect of CartoonGAN.The corrected images are blended with the originals to retain aesthetic appeal and visual distinctiveness,resulting in improved color accuracy and overall coherence.Experimental results demonstrate that EPI significantly increases similarity to original input images compared to the standard CartoonGAN model,achieving a 40.14%enhancement in visual similarity in Learned Perceptual Image Patch Similarity(LPIPS),a 30.21%improvement in structural consistency in Structural Similarity Index Measure(SSIM),and an 11.81%reduction in pixel-level error in Mean Squared Error(MSE).By addressing limitations present in the traditional CartoonGAN pipeline,EPI offers practical enhancements for creative applications,particularly within media and design fields where visual fidelity and artistic style preservation are critical.These improvements align with the goals of Fog and Edge Computing,which also seek to enhance processing efficiency and application performance in sensitive industries such as healthcare,logistics,and education.This research not only resolves key deficiencies in existing CartoonGAN models but also expands its potential applications in image-based content creation,bridging gaps between technical constraints and creative demands.Future studies may explore the adaptability of EPI across various datasets and artistic styles,potentially broadening its impact on visual transformation tasks.
基金supported by the Medium and Long-term Science and Technology Plan for Radio,Television,and Online Audiovisuals(2023AC0200)the Public Welfare Technology Application Research Project of Zhejiang Province,China(No.LGF21F010001).
文摘Recent deep neural network(DNN)based blind image quality assessment(BIQA)approaches take mean opinion score(MOS)as ground-truth labels,which would lead to cross-datasets biases and limited generalization ability of the DNN-based BIQA model.This work validates the natural instability of MOS through investigating the neuropsychological characteristics inside the human visual system during quality perception.By combining persistent homology analysis with electroencephalogram(EEG),the physiologically meaningful features of the brain responses to different distortion levels are extracted.The physiological features indicate that although volunteers view exactly the same image content,their EEG features are quite varied.Based on the physiological results,we advocate treating MOS as noisy labels and optimizing the DNN based BIQA model with earlystop strategies.Experimental results on both innerdataset and cross-dataset demonstrate the superiority of our optimization approach in terms of generalization ability.
基金supported by the National Natural Science Foundation of China(Nos.11905028 and 12105040)Scientific Research Project of the Education Department of Jilin Province(No.JJKH20231294KJ)the Youth Growth Technology Project of the Science and Technology Department of Jilin Province(No.20210508027RQ).
文摘Neutron radiographic images(NRIs)typically suffer from multiple distortions,including various types of noise,geometric unsharpness,and white spots.Image quality assessment(IQA)can guide on-site image screening and even provide metrics for subsequent image processing.However,existing IQA methods for NRIs cannot effectively evaluate the quality of real NRIs with a specific distortion of white spots,limiting their practical application.In this paper,a novel no-reference IQA method is proposed to comprehensively evaluate the quality of real NRIs with multiple distortions.First,we construct large-scale NRI datasets with more than 20,000 images,including high-quality original NRIs and synthetic NRIs with various distortions.Next,an image quality calibration method based on visual salience and a local quality map is introduced to label the NRI dataset with quality scores.Finally,a lightweight convolutional neural network(CNN)model is designed to learn the abstract relationship between the NRIs and quality scores using the constructed NRI training dataset.Extensive experimental results demonstrate that the proposed method exhibits good consistency with human visual perception when evaluating both real NRIs and processed NRIs using enhancement and restoration algorithms,highlighting its application potential.
基金supported by the First Affiliated Hospital of Xi’an Jiaotong University Teaching Reform Project(Grant No.JG2023-0206 and JG2022-0324).
文摘Objective:In the Radiology Department of Mzuzu Central Hospital(MCH),daily training for radiographers now includes content on Computed Tomography(CT)image quality control and equipment maintenance to ensure the normal,continuous,and stable operation of the 16-slice spiral CT scanner.Methods:Through comprehensive analysis of relevant equipment,we have identified key parameters that significantly impact CT image quality.Innovative optimization strategies and solutions targeting these parameters have been developed and integrated into daily training programs.Furthermore,starting from an examination of prevalent failure modes observed in CT equipment,we delve into essential maintenance and preservation techniques that CT technologists must master to ensure optimal system performance.Results:(1)Crucial factors affecting CT image quality include artifacts,noise,partial volume effects,and surrounding gap phenomena,alongside spatial and density resolutions,CT dose,reconstruction algorithms,and human factors during the scanning process.In the daily training for radiographers,emphasis is placed on strictly implementing image quality control measures at every stage of the CT scanning process and skillfully applying advanced scanning and image processing techniques.By doing so,we can provide clinicians with accurate and reliable imaging references for diagnosis and treatment.(2)Strategies for CT equipment maintenance:①Environmental inspection of the CT room to ensure cleanliness and hygiene.②Rational and accurate operation,including calibration software proficiency.③Regular maintenance and servicing for minimizing machine downtime.④Maintenance of the CT X-ray tube.CT technicians can become proficient in equipment maintenance and upkeep techniques through training,which can significantly extend the service life of CT systems and reduce the occurrence of malfunctions.Conclusion:Through the regular implementation of rigorous CT image quality control training for radiology technicians,coupled with diligent and proactive CT equipment maintenance,we have observed profound and beneficial impacts on improving image quality.The accuracy and fidelity of radiological data ultimately leads to more accurate diagnoses and effective treatments.
基金supported by the National Natural Science Foundation of China(Nos.62276210,82201148 and 62376215)the Key Research and Development Project of Shaanxi Province(No.2025CY-YBXM-044)+3 种基金the Natural Science Foundation of Zhejiang Province(No.LQ22H120002)the Medical Health Science and Technology Project of Zhejiang Province(Nos.2022RC069 and 2023KY1140)the Natural Science Foundation of Ningbo(No.2023J390)the Ningbo Top Medical and Health Research Program(No.2023030716).
文摘This paper proposes a novel method for the automatic diagnosis of keratitis using feature vector quantization and self-attention mechanisms(ADK_FVQSAM).First,high-level features are extracted using the DenseNet121 backbone network,followed by adaptive average pooling to scale the features to a fixed length.Subsequently,product quantization with residuals(PQR)is applied to convert continuous feature vectors into discrete features representations,preserving essential information insensitive to image quality variations.The quantized and original features are concatenated and fed into a self-attention mechanism to capture keratitis-related features.Finally,these enhanced features are classified through a fully connected layer.Experiments on clinical low-quality(LQ)images show that ADK_FVQSAM achieves accuracies of 87.7%,81.9%,and 89.3% for keratitis,other corneal abnormalities,and normal corneas,respectively.Compared to DenseNet121,Swin transformer,and InceptionResNet,ADK_FVQSAM improves average accuracy by 3.1%,11.3%,and 15.3%,respectively.These results demonstrate that ADK_FVQSAM significantly enhances the recognition performance of keratitis based on LQ slit-lamp images,offering a practical approach for clinical application.
基金supported by the Fundamental Research Funds for the Central Universities.
文摘Most blind image quality assessment(BIQA)methods require a large amount of time to collect human opinion scores as training labels,which limits their usability in practice.Thus,we present an opinion-unaware BIQA method based on deep reinforcement learning which is trained without subjective scores,named DRL-IQA.Inspired by the human visual perception process,our model is formulated as a quality reinforced agent,which consists of the dynamic distortion generation part and the quality perception part.By considering the image distortion degradation process as a sequential decision-making process,the dynamic distortion generation part can develop a strategy to add as many different distortions as possible to an image,which enriches the distortion space to alleviate overfitting.A reward function calculated from quality degradation after adding distortion is utilized to continuously optimize the strategy.Furthermore,the quality perception part can extract rich quality features from the quality degradation process without using subjective scores,and accurately predict the state values that represent the image quality.Experimental results reveal that our method achieves competitive quality prediction performance compared to other state-of-the-art BIQA methods.
基金supported by the National Natural Science Foundation of China(NSFC)12333010the National Key R&D Program of China 2022YFF0503002+3 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(grant No.XDB0560000)the NSFC 11921003supported by the Prominent Postdoctoral Project of Jiangsu Province(2023ZB304)supported by the Strategic Priority Research Program on Space Science,the Chinese Academy of Sciences,grant No.XDA15320000.
文摘Indirect X-ray modulation imaging has been adopted in a number of solar missions and provided reconstructed X-ray images of solar flares that are of great scientific importance.However,the assessment of the image quality of the reconstruction is still difficult,which is particularly useful for scheme design of X-ray imaging systems,testing and improvement of imaging algorithms,and scientific research of X-ray sources.Currently,there is no specified method to quantitatively evaluate the quality of X-ray image reconstruction and the point-spread function(PSF)of an X-ray imager.In this paper,we propose percentage proximity degree(PPD)by considering the imaging characteristics of X-ray image reconstruction and in particular,sidelobes and their effects on imaging quality.After testing a variety of imaging quality assessments in six aspects,we utilized the technique for order preference by similarity to ideal solution to the indices that meet the requirements.Then we develop the final quality index for X-ray image reconstruction,QuIX,which consists of the selected indices and the new PPD.QuIX performs well in a series of tests,including assessment of instrument PSF and simulation tests under different grid configurations,as well as imaging tests with RHESSI data.It is also a useful tool for testing of imaging algorithms,and determination of imaging parameters for both RHESSI and ASO-S/Hard X-ray Imager,such as field of view,beam width factor,and detector selection.
基金supported by the National Natural Science Foundation of China(NSFC)under grant No.12233012,the Strategic Priority Research Program of the Chinese Academy of Sciences,grant No.XDB0560102the National Key R&D Program of China 2022YFF0503003(2022YFF0503000)。
文摘The in-flight calibration and performance of the Solar Disk Imager(SDI),which is a pivotal instrument of the LyαSolar Telescope onboard the Advanced Space-based Solar Observatory mission,suggested a much lower spatial resolution than expected.In this paper,we developed the SDI point-spread function(PSF)and Image Bivariate Optimization Algorithm(SPIBOA)to improve the quality of SDI images.The bivariate optimization method smartly combines deep learning with optical system modeling.Despite the lack of information about the real image taken by SDI and the optical system function,this algorithm effectively estimates the PSF of the SDI imaging system directly from a large sample of observational data.We use the estimated PSF to conduct deconvolution correction to observed SDI images,and the resulting images show that the spatial resolution after correction has increased by a factor of more than three with respect to the observed ones.Meanwhile,our method also significantly reduces the inherent noise in the observed SDI images.The SPIBOA has now been successfully integrated into the routine SDI data processing,providing important support for the scientific studies based on the data.The development and application of SPIBOA also paves new ways to identify astronomical telescope systems and enhance observational image quality.Some essential factors and precautions in applying the SPIBOA method are also discussed.
基金National Natural Science Foundation Major Project of China,Grant/Award Number:42192580Guangdong Province Key Construction Discipline Scientific Research Ability Promotion Project,Grant/Award Number:2022ZDJS015。
文摘In Hyperspectral Imaging(HSI),the detrimental influence of noise and distortions on data quality is profound,which has severely affected the following-on analytics and decisionmaking such as land mapping.This study presents an innovative framework for assessing HSI band quality and reconstructing the low-quality bands,based on the Prophet model.By introducing a comprehensive quality metric to start,the authors approach factors in both spatial and spectral characteristics across local and global scales.This metric effectively captures the intricate noise and distortions inherent in the HSI data.Subsequently,the authors employ the Prophet model to forecast information within the low-quality bands,leveraging insights from neighbouring high-quality bands.To validate the effectiveness of the authors’proposed model,extensive experiments on three publicly available uncorrected datasets are conducted.In a head-to-head comparison,the framework against six state-ofthe-art band reconstruction algorithms including three spectral methods,two spatialspectral methods and one deep learning method is benchmarked.The authors’experiments also delve into strategies for band selection based on quality metrics and the quality evaluation of the reconstructed bands.In addition,the authors assess the classification accuracy utilising these reconstructed bands.In various experiments,the results consistently affirm the efficacy of the authors’method in HSI quality assessment and band reconstruction.Notably,the authors’approach obviates the need for manually prefiltering of noisy bands.This comprehensive framework holds promise in addressing HSI data quality concerns whilst enhancing the overall utility of HSI.
文摘Air quality estimation assesses the pollution level in the air,supports public health warnings,and is a valuable tool in environmental management.Although air sensors have proven helpful in this task,sensors are often expensive and difficult to install,while cameras are becoming more popular and accessible,from which images can be collected as data for deep learning models to solve the above task.This leads to another problem:several labeled images are needed to achieve high accuracy when deep-learningmodels predict air quality.In this research,we have threemain contributions:(1)Collect and publish an air quality estimation dataset,namely PTIT_AQED,including environmental image data and air quality;(2)Propose a deep learning model to predict air quality with few data,called PTIT_FAQE(PTIT Few-shot air quality estimation).We build PTIT_FAQE based on EfficientNet-a CNN architecture that ensures high performance in deep learning applications and Few-shot Learning with Prototypical Networks.This helps the model use only a fewtraining data but still achieve high accuracy in air quality estimation.And(3)conduct experiments to prove the superiority of PTIT_FAQE compared to other studies on both PTIT_AQED and APIN datasets.The results show that our model achieves an accuracy of 0.9278 and an F1-Score of 0.9139 on the PTIT_AQED dataset and an accuracy of 0.9467 and an F1-Score of 0.9371 on the APIN dataset,which demonstrate a significant performance improvement compared to previous studies.We also conduct detailed experiments to evaluate the impact of each component on model performance.
基金supported by the National Natural Science Foundation of China(No.82204610)Qihang Talent Program(L2022046)+1 种基金the Scientific and Technological Innovation Project of China Academy of Chinese Medical Sciences(CI2021A04013)Fundamental Research Funds for the Central Public Welfare Research Institutes(ZZ15-YQ-041 and L2021029).
文摘Background:Cyperi Rhizoma,derived from Cyperus rotundus L.,is a widely used medicinal herb in traditional Chinese medicine(TCM),with Shandong Province recognized as its geo-authentic habitat.However,the quality of Cyperi Rhizoma varies significantly across different regions,potentially influencing its therapeutic efficacy.This study investigates the influence of geographic origin on the chemical composition and overall quality of Cyperi Rhizoma.Methods:A comprehensive approach,including traditional quality assessment,GC-MS(g as c hromatography-m ass s pectrometry),RP-HPLC(r everse p hase h igh-p erformance l iquid c hromatography),and FT-IR(f ourier t ransform i nfrared s pectroscopy)techniques,was employed to analyze Cyperi Rhizoma samples from Shandong Province.These methods examined the physical appearance,chemical profile,and content variations,particularly focusing onα-cyperone.Results:Traditional quality assessments revealed noticeable differences in the external characteristics of the samples.GC-MS analysis identified a variety of unique chemical constituents,while RP-HPLC and FT-IR showed significant variations inα-cyperone content,with higher levels found in Shandong samples.Conclusion:These results demonstrate that geographic origin is a critical determinant of Cyperi Rhizoma quality,with Shandong specimens exhibiting superiorα-cyperone levels and characteristic phytochemical profiles.This validates the geo-authenticity concept in TCM and provides actionable data for developing evidence-based quality standards,suggesting that provenance should be prioritized in medicinal material selection and pharmacopeial specifications.
基金supported by Postgraduate Research&Practice Innovation Program of Jiangsu Province,China(Grant No.SJCX24_1332)Jiangsu Province Education Science Planning Project in 2024(Grant No.B-b/2024/01/122)High-Level Talent Scientific Research Foundation of Jinling Institute of Technology,China(Grant No.jit-b-201918).
文摘Digital watermarking technology plays an important role in detecting malicious tampering and protecting image copyright.However,in practical applications,this technology faces various problems such as severe image distortion,inaccurate localization of the tampered regions,and difficulty in recovering content.Given these shortcomings,a fragile image watermarking algorithm for tampering blind-detection and content self-recovery is proposed.The multi-feature watermarking authentication code(AC)is constructed using texture feature of local binary patterns(LBP),direct coefficient of discrete cosine transform(DCT)and contrast feature of gray level co-occurrence matrix(GLCM)for detecting the tampered region,and the recovery code(RC)is designed according to the average grayscale value of pixels in image blocks for recovering the tampered content.Optimal pixel adjustment process(OPAP)and least significant bit(LSB)algorithms are used to embed the recovery code and authentication code into the image in a staggered manner.When detecting the integrity of the image,the authentication code comparison method and threshold judgment method are used to perform two rounds of tampering detection on the image and blindly recover the tampered content.Experimental results show that this algorithm has good transparency,strong and blind detection,and self-recovery performance against four types of malicious attacks and some conventional signal processing operations.When resisting copy-paste,text addition,cropping and vector quantization under the tampering rate(TR)10%,the average tampering detection rate is up to 94.09%,and the peak signal-to-noise ratio(PSNR)of the watermarked image and the recovered image are both greater than 41.47 and 40.31 dB,which demonstrates its excellent advantages compared with other related algorithms in recent years.
基金funded by the National Natural Science Foundation of China(NNSFC)under Grant Numbers 42322408,42188101,and 42441809Additional support was provided by the Climbing Program of the National Space Science Center(NSSC,Grant No.E4PD3005)as well as the Specialized Research Fund for State Key Laboratories of China.
文摘A large-scale view of the magnetospheric cusp is expected to be obtained by the Soft X-ray Imager(SXI)onboard the Solar wind Magnetosphere Ionosphere Link Explorer(SMILE).However,it is challenging to trace the three-dimensional cusp boundary from a two-dimensional X-ray image because the detected X-ray signals will be integrated along the line of sight.In this work,a global magnetohydrodynamic code was used to simulate the X-ray images and photon count images,assuming an interplanetary magnetic field with a pure Bz component.The assumption of an elliptic cusp boundary at a given altitude was used to trace the equatorward and poleward boundaries of the cusp from a simulated X-ray image.The average discrepancy was less than 0.1 RE.To reduce the influence of instrument effects and cosmic X-ray backgrounds,image denoising was considered before applying the method above to SXI photon count images.The cusp boundaries were reasonably reconstructed from the noisy X-ray image.
基金funded by the National Natural Science Foundation of China,grant numbers 52374156 and 62476005。
文摘Images taken in dim environments frequently exhibit issues like insufficient brightness,noise,color shifts,and loss of detail.These problems pose significant challenges to dark image enhancement tasks.Current approaches,while effective in global illumination modeling,often struggle to simultaneously suppress noise and preserve structural details,especially under heterogeneous lighting.Furthermore,misalignment between luminance and color channels introduces additional challenges to accurate enhancement.In response to the aforementioned difficulties,we introduce a single-stage framework,M2ATNet,using the multi-scale multi-attention and Transformer architecture.First,to address the problems of texture blurring and residual noise,we design a multi-scale multi-attention denoising module(MMAD),which is applied separately to the luminance and color channels to enhance the structural and texture modeling capabilities.Secondly,to solve the non-alignment problem of the luminance and color channels,we introduce the multi-channel feature fusion Transformer(CFFT)module,which effectively recovers the dark details and corrects the color shifts through cross-channel alignment and deep feature interaction.To guide the model to learn more stably and efficiently,we also fuse multiple types of loss functions to form a hybrid loss term.We extensively evaluate the proposed method on various standard datasets,including LOL-v1,LOL-v2,DICM,LIME,and NPE.Evaluation in terms of numerical metrics and visual quality demonstrate that M2ATNet consistently outperforms existing advanced approaches.Ablation studies further confirm the critical roles played by the MMAD and CFFT modules to detail preservation and visual fidelity under challenging illumination-deficient environments.
文摘AIM:To assess the visual acuity and visual quality of cataract patients who received femtosecond laser-assisted cataract surgery(FLACS)and multifocal intraocular lens(MIOL)implantation with an improved polishing technique during a 1-year follow-up period.METHODS:This study included 74 eyes from 37 patients,comprising 17 males and 20 females,with a mean age of 51.74±7.80 years.Using a coin toss method,one eye per patient was randomly selected for improved anterior capsular polishing,while the other eye received standard irrigation/aspiration(I/A)polishing.The polishing group consisted of 37 eyes,including 21 right and 16 left eyes,while the control group comprised the contralateral fellow eyes of the same individuals in the polishing group.Visual acuity and quality of the patients were evaluated before surgery and at 1 wk,1,6,and 12 mo after surgery.The OPD-Scan III was utilized to assess high-order aberrations,while the optical quality analysis system(OQAS)was employed to evaluate modulation transfer function(MTF cutoff),Strehl ratio(SR),and objective scatter index(OSI)for the purpose of visual quality assessment.Paired t-tests and repeated measures analysis of variance(ANOVA)were utilized to compare the results,and the SNK-q post hoc test was applied to identify significant differences.RESULTS:The polishing group’s uncorrected distant visual acuity(UDVA)and uncorrected near visual acuity(UNVA)significantly improved 1-week post-surgery(all P<0.05).At 12-months,UDVA,UNVA,and corrected distant visual acuity(CDVA)were better than the control group(P<0.05).The MTF cutoff,SR,OSI,and high-order aberrations in the polishing group also showed significant improvements compared to the control group at 12 mo(P<0.05).CONCLUSION:The improved capsular polishing method has been demonstrated to effectively maintain visual acuity and visual quality in patients with MIOL after FLACS within 1 a.
文摘BACKGROUND Dry eye disease(DED)is a multifactorial ocular surface disorder with rising prevalence.It is closely related to systemic health and psychological factors,such as sleep and mood disorders,which significantly impact the quality of life of patients.AIM To explore the correlations between ocular surface function,sleep quality,and anxiety/depression in patients with DED.METHODS This was a cross-sectional investigative study that included 358 patients with DED between January 2022 and January 2025.Ocular surface was assessed using the ocular surface disease index(OSDI),tear film break-up time,fluorescein staining score,and Schirmer I test.The Pittsburgh Sleep Quality Index(PSQI),Self-Rating Anxiety Scale(SAS),and Self-Rating Depression Scale(SDS)were used to evaluate sleep quality and anxiety/depression levels.Correlation and linear regression analyses were used to explore the relationships.RESULTS The mean PSQI score of the patients was 9.94±2.18;the mean SAS score was 47.30±4.90,and the mean SDS score was 50.08±5.52.These suggested a prevalence of sleep and psychological abnormalities.There was a significant correlation between the indicators of ocular surface function(OSDI,tear film break-up time,fluorescein staining,and Schirmer I test)and PSQI,SAS,and SDS scores(P<0.05).Moreover,multiple regression revealed that age≥50 years(β=1.55,P=0.029),PSQI scores(β=0.58,P<0.001),SAS scores(β=0.17,P=0.017),and SDS scores(β=0.15,P=0.019)were independent predictors of the OSDI scores.CONCLUSION Ocular surface function in patients with DED is closely related to sleep quality and anxiety/depression,emphasizing the need for holistic clinical management.
基金provided by the Science Research Project of Hebei Education Department under grant No.BJK2024115.
文摘High-resolution remote sensing images(HRSIs)are now an essential data source for gathering surface information due to advancements in remote sensing data capture technologies.However,their significant scale changes and wealth of spatial details pose challenges for semantic segmentation.While convolutional neural networks(CNNs)excel at capturing local features,they are limited in modeling long-range dependencies.Conversely,transformers utilize multihead self-attention to integrate global context effectively,but this approach often incurs a high computational cost.This paper proposes a global-local multiscale context network(GLMCNet)to extract both global and local multiscale contextual information from HRSIs.A detail-enhanced filtering module(DEFM)is proposed at the end of the encoder to refine the encoder outputs further,thereby enhancing the key details extracted by the encoder and effectively suppressing redundant information.In addition,a global-local multiscale transformer block(GLMTB)is proposed in the decoding stage to enable the modeling of rich multiscale global and local information.We also design a stair fusion mechanism to transmit deep semantic information from deep to shallow layers progressively.Finally,we propose the semantic awareness enhancement module(SAEM),which further enhances the representation of multiscale semantic features through spatial attention and covariance channel attention.Extensive ablation analyses and comparative experiments were conducted to evaluate the performance of the proposed method.Specifically,our method achieved a mean Intersection over Union(mIoU)of 86.89%on the ISPRS Potsdam dataset and 84.34%on the ISPRS Vaihingen dataset,outperforming existing models such as ABCNet and BANet.
文摘BACKGROUND Approximately 30%of patients with head and neck cancer experience adverse effects caused by anxiety and depression.Considering the high prevalence,implementing customized interventions to ease adverse emotional states is imperative.AIM To evaluate the efficacy of cognitive behavioral therapy(CBT)-based psychological interventions in improving the psychological well-being and quality of life(QoL)of patients with laryngeal carcinoma.METHODS This study enrolled 120 patients admitted from February 2022 to February 2024.The control group,comprising 50 participants,received standard supportive psychological care,while the research group,consisting 70 participants,underwent CBT-based interventions.Several clinical outcomes were systematically assessed that included postoperative recovery metrics(duration of tracheostomy and nasogastric tube dependence and length of hospitalization),psychological status(Self-Rating Anxiety Scale and Self-Rating Depression Scale),nutritional markers(serum albumin and hemoglobin levels),sleep quality(Self-Rating Scale of Sleep and Athens Insomnia Scale),and QoL(Functional Assessment of Cancer Therapy-Head and Neck).RESULTS The results demonstrated that the research group experienced superior outcomes,with significantly reduced durations of tracheostomy and nasogastric tube dependence,as well as shorter hospital stays,compared with the control group.Additionally,the research group exhibited markedly lower post-intervention Self-Rating Anxiety Scale,Self-Rating Depression Scale,Self-Rating Scale of Sleep,and Athens Insomnia Scale scores,along with minimal but higher change in serum albumin and hemoglobin levels compared with the control group.All five domains of Functional Assessment of Cancer Therapy-Head and Neck showed notable improvements in the research group,exceeding those observed in the control group.CONCLUSION CBT-based psychological support positively affects the mental well-being and QoL of patients with laryngeal carcinoma,highlighting its potential for broader clinical application.
文摘Chinese President Xi Jinping has guided China through a year of resilient growth via forward-looking reforms and innovation-driven transformation that is shaping the nation’s economic trajectory for 2026 and beyond.
