In recent years,deep learning has been introduced into the field of Single-pixel imaging(SPI),garnering significant attention.However,conventional networks still exhibit limitations in preserving image details.To addr...In recent years,deep learning has been introduced into the field of Single-pixel imaging(SPI),garnering significant attention.However,conventional networks still exhibit limitations in preserving image details.To address this issue,we integrate Large Kernel Convolution(LKconv)into the U-Net framework,proposing an enhanced network structure named U-LKconv network,which significantly enhances the capability to recover image details even under low sampling conditions.展开更多
The energy of light exposed on human skin is compulsively limited for safety reasons which affects the power of photoacoustic （PA） signal and its signal-to-noise ratio （SNR） level. Thus, the final reconstructed PA...The energy of light exposed on human skin is compulsively limited for safety reasons which affects the power of photoacoustic （PA） signal and its signal-to-noise ratio （SNR） level. Thus, the final reconstructed PA image quality is degraded. This Letter proposes an adaptive multi-sample-based approach to enhance the SNR of PA signals and in addition, detailed information in rebuilt PA images that used to be buried in the noise can be distinguished. Both ex vivo and in vivo experiments are conducted to validate the effectiveness of our proposed method which provides its potential value in clinical trials.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
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
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
Purpose:To assess the clinical efficacy of integrating deep learning reconstruction(DLR)with contrast-enhancement-boost(CE-boost)in 80 kVp head and neck CT angiography(CTA)using substantially lowered radiation and con...Purpose:To assess the clinical efficacy of integrating deep learning reconstruction(DLR)with contrast-enhancement-boost(CE-boost)in 80 kVp head and neck CT angiography(CTA)using substantially lowered radiation and contrast medium(CM)doses,compared to the standard 100 kVp protocol using hybrid iterative reconstruction(HIR).Methods:Sixty-six patients were prospectively enrolled and randomly assigned to one of two groups:the low-dose group(n=33),receiving 80 kVp and 28 mL contrast medium(CM)with a noise index(NI)of 15;and the regular-dose group(n=33),receiving 100 kVp and 40 mL CM with an NI of 10.For the lowdose group,images underwent reconstruction using both hybrid iterative reconstruction(HIR)and deep learning reconstruction(DLR)at mild-,standard-,and strong-strength levels,both before and after combination with contrast enhancement-boost(CE-boost).This generated eight distinct datasets:L-HIR,L-DLR_(mild),L-DLR_(standard),L-DLR_(strong),L-HIR-CE,L-DLR_(mild)-CE,L-DLR_(standard)-CE,and L-DLR_(strong)-CE.Images for the regular-dose group were reconstructed solely with HIR(R-HIR).Quantitative analysis involved calculating and comparing CT attenuation,image noise,signal-to-noise ratio(SNR),and contrast-to-noise ratio(CNR)within six key vessels:the aortic arch(AA),internal carotid artery(ICA),external carotid artery(ECA),vertebral arteries(VA),basilar artery(BA),and middle cerebral artery(MCA).Two radiologists independently assessed subjective image quality using a 5-point scale,with statistical significance defined as P<0.05.Results:Compared to the regular-dose group,the low-dose protocol achieved a substantial reduction in contrast media volume(28 mL versus 40 mL,a 30%decrease)and radiation exposure((0.41±0.08)mSv versus(1.18±0.12)mSv,a 65%reduction).Both L-DLR_(standard) and L-DLR_(strong) delivered comparable or superior SNR and CNR across all vascular segments relative to R-HIR.However,subjective image quality scores for L-DLR at all strength levels fell below those for R-HIR(all P<0.05 for both readers).Combining CE-boost with the low-dose protocol significantly enhanced the objective image performance of L-DLR_(strong)-CE(all P<0.05)and produced subjective image scores comparable to R-HIR(reader 1:P=0.15;reader 2:P=0.06).Conclusion:When compared to the standard 100 kVp head and neck CTA,the combination of the DLR and CE-boost techniques at 80 kVp can achieve a 30%reduction in contrast dose and a 65%reduction in radiation dose,while maintaining both objective and subjective image quality.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
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
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
The coherent-mode representation theory is firstly used to analyze lensless two-color ghost imaging. A quite complicated expression about the point-spread function（PSF） needs to be given to analyze which wavelength ...The coherent-mode representation theory is firstly used to analyze lensless two-color ghost imaging. A quite complicated expression about the point-spread function（PSF） needs to be given to analyze which wavelength has a stronger affect on imaging quality when the usual integral representation theory is used to ghost imaging. Unlike this theory, the coherent-mode representation theory shows that imaging quality depends crucially on the distribution of the decomposition coefficients of the object imaged in a two-color ghost imaging. The analytical expression of the decomposition coefficients of the object is unconcerned with the wavelength of the light used in the reference arm, but has relevance with the wavelength in the object arm. In other words, imaging quality of two-color ghost imaging depends primarily on the wavelength of the light illuminating the object. Our simulation results also demonstrate this conclusion.展开更多
To further explore the human visual system（ HVS）,the perceptual grouping（ PG）, which has been proven to play an important role in the HVS, is adopted to design an effective image quality assessment（ IQA） model. ...To further explore the human visual system（ HVS）,the perceptual grouping（ PG）, which has been proven to play an important role in the HVS, is adopted to design an effective image quality assessment（ IQA） model. Compared with the existing fixed-window-based models, the proposed one is an adaptive window-like model that introduces the perceptual grouping strategy into the IQA model. It works as follows： first,it preprocesses the images by clustering similar pixels into a group to the greatest extent; then the structural similarity is used to compute the similarity of the superpixels between reference and distorted images; finally, it integrates all the similarity of superpixels of an image to yield a quality score. Experimental results on three databases（ LIVE, IVC and MICT） showthat the proposed method yields good performance in terms of correlation with human judgments of visual quality.展开更多
Imaging quality is a critical component of compressive imaging in real applications. In this study, we propose a compressive imaging method based on multi-scale modulation and reconstruction in the spatial frequency d...Imaging quality is a critical component of compressive imaging in real applications. In this study, we propose a compressive imaging method based on multi-scale modulation and reconstruction in the spatial frequency domain. Theoretical analysis and simulation show the relation between the measurement matrix resolution and compressive sensing(CS)imaging quality. The matrix design is improved to provide multi-scale modulations, followed by individual reconstruction of images of different spatial frequencies. Compared with traditional single-scale CS imaging, the multi-scale method provides high quality imaging in both high and low frequencies, and effectively decreases the overall reconstruction error.Experimental results confirm the feasibility of this technique, especially at low sampling rate. The method may thus be helpful in promoting the implementation of compressive imaging in real applications.展开更多
AIM: To compare the effect of oral erythromycin vs no preparation with prokinetics on the transit time and the image quality of capsule endoscopy (CE) in evaluating small bowel (SB) pathology. METHODS: We conducted a ...AIM: To compare the effect of oral erythromycin vs no preparation with prokinetics on the transit time and the image quality of capsule endoscopy (CE) in evaluating small bowel (SB) pathology. METHODS: We conducted a retrospective, blinded (to the type of preparation) review of 100 CE studies, 50 with no preparation with prokinetics from one medical center (Group A) and 50 from another center with administration of a single dose of 200 mg oral erythromycin 1 h prior to CE (Group B). Gastric, SB and total transit times were calculated, the presence of bile in the duodenum was scored, as was cleanliness within the proximal, middle and distal intestine. RESULTS: The erythromycin group had a slightly shorter gastric transit time (21 min vs 28 min, with no statistical significance). SB transit time was similar for both groups (all P > 0.05). Total transit time was almost identical in both groups. The rate of incomplete examination was 16% for Group A and 10% for Group B (P = 0.37). Bile and cleanliness scores in different parts of the intestine were similar for the two groups (P > 0.05). CONCLUSION: Preparation for capsule endoscopy with erythromycin does not affect SB or total transit time. It tends to reduce gastric transit time, but it does not increase the cecum-reaching rate. Erythromycin does not adversely affect image quality. We consider the routine use of oral erythromycin preparation as being unjustified, although it might be considered in patients with known prolonged gastric emptying time.展开更多
Neural network methods have recently emerged as a hot topic in computed tomography(CT) imaging owing to their powerful fitting ability;however, their potential applications still need to be carefully studied because t...Neural network methods have recently emerged as a hot topic in computed tomography(CT) imaging owing to their powerful fitting ability;however, their potential applications still need to be carefully studied because their results are often difficult to interpret and are ambiguous in generalizability. Thus, quality assessments of the results obtained from a neural network are necessary to evaluate the neural network. Assessing the image quality of neural networks using traditional objective measurements is not appropriate because neural networks are nonstationary and nonlinear. In contrast, subjective assessments are trustworthy, although they are time-and energy-consuming for radiologists. Model observers that mimic subjective assessment require the mean and covariance of images, which are calculated from numerous image samples;however, this has not yet been applied to the evaluation of neural networks. In this study, we propose an analytical method for noise propagation from a single projection to efficiently evaluate convolutional neural networks(CNNs) in the CT imaging field. We propagate noise through nonlinear layers in a CNN using the Taylor expansion. Nesting of the linear and nonlinear layer noise propagation constitutes the covariance estimation of the CNN. A commonly used U-net structure is adopted for validation. The results reveal that the covariance estimation obtained from the proposed analytical method agrees well with that obtained from the image samples for different phantoms, noise levels, and activation functions, demonstrating that propagating noise from only a single projection is feasible for CNN methods in CT reconstruction. In addition, we use covariance estimation to provide three measurements for the qualitative and quantitative performance evaluation of U-net. The results indicate that the network cannot be applied to projections with high noise levels and possesses limitations in terms of efficiency for processing low-noise projections. U-net is more effective in improving the image quality of smooth regions compared with that of the edge. LeakyReLU outperforms Swish in terms of noise reduction.展开更多
Objective To retrospectively evaluate the effects of saline administration following contrast material injection, abdominal compression and two delay phase acquisition on image quality improvement of computed tomograp...Objective To retrospectively evaluate the effects of saline administration following contrast material injection, abdominal compression and two delay phase acquisition on image quality improvement of computed tomographic urography (CTU). Methods Medical records and informed consents of patients were obtained. In totally 122 patients (50 men, 72 women), two delay phase images with CTU were performed. Scans began simultaneously with a contrast bolus injection of 100 mL (300 mgI/mL) followed by a saline bolus injection of 100 mL at a rate of 5 mL/s. Two delay phase images were taken at 400 and 550 seconds for each patient. Examinations were taken by using abdominal compression or not. The distention and opacification of the urinary tract were evaluated by two interpreters together on transverse images and post-processing images. Effects of four techniques (saline administration and abdominal compression, saline administration only, compression only, and neither saline administration nor compression) and two delay phase acquisition on image quality improvement were analysed by using ANOVA and Chi-square test. Results Saline administration improved opacification (P<0.05) and increased overall image quality (P<0.01) of the intrarenal collecting system and proximal ureter. Abdominal compression (P<0.05) and delayed phase image acquisition of 550 seconds (P<0.01) all improved distention of the intrarenal collecting system and proximal ureter but did not improve opacification. No statistically significant effects on the distal ureter were found. However, there were more visualized distal ureteral segments with the longer imaging delay. Conclusion Saline administration, abdominal compression and longer imaging delays are all effective in improving image quality of 64-detector row CTU.展开更多
Multi-modality medical image fusion has more and more important applications in medical image analysis and understanding. In this paper, we develop and apply a multi-resolution method based on wavelet pyramid to fuse ...Multi-modality medical image fusion has more and more important applications in medical image analysis and understanding. In this paper, we develop and apply a multi-resolution method based on wavelet pyramid to fuse medical images from different modalities such as PET-MRI and CT-MRI. In particular, we evaluate the different fusion results when applying different selection rules and obtain optimum combination of fusion parameters.展开更多
In this paper,a new approach is proposed to determine whether the content of an image is authentic or modified with a focus on detecting complex image tampering.Detecting image tampering without any prior information ...In this paper,a new approach is proposed to determine whether the content of an image is authentic or modified with a focus on detecting complex image tampering.Detecting image tampering without any prior information of the original image is a challenging problem since unknown diverse manipulations may have different characteristics and so do various formats of images.Our principle is that image processing,no matter how complex,may affect image quality,so image quality metrics can be used to distinguish tampered images.In particular,based on the alteration of image quality in modified blocks,the proposed method can locate the tampered areas.Referring to four types of effective no-reference image quality metrics,we obtain 13 features to present an image.The experimental results show that the proposed method is very promising on detecting image tampering and locating the locally tampered areas especially in realistic scenarios.展开更多
Blind image quality assessment(BIQA)is of fundamental importance in low-level computer vision community.Increasing interest has been drawn in exploiting deep neural networks for BIQA.Despite of the notable success ach...Blind image quality assessment(BIQA)is of fundamental importance in low-level computer vision community.Increasing interest has been drawn in exploiting deep neural networks for BIQA.Despite of the notable success achieved,there is a broad consensus that training deep convolutional neural networks(DCNN)heavily relies on massive annotated data.Unfortunately,BIQA is typically a small sample problem,resulting the generalization ability of BIQA severely restricted.In order to improve the accuracy and generalization ability of BIQA metrics,this work proposed a totally opinion-unaware BIQA in which no subjective annotations are involved in the training stage.Multiple full-reference image quality assessment(FR-IQA)metrics are employed to label the distorted image as a substitution of subjective quality annotation.A deep neural network(DNN)is trained to blindly predict the multiple FR-IQA score in absence of corresponding pristine image.In the end,a selfsupervised FR-IQA score aggregator implemented by adversarial auto-encoder pools the predictions of multiple FR-IQA scores into the final quality predicting score.Even though none of subjective scores are involved in the training stage,experimental results indicate that our proposed full reference induced BIQA framework is as competitive as state-of-the-art BIQA metrics.展开更多
It is well-known that classical quality measures,such as Mean Squared Error(MSE),Weighted Mean Squared Error(WMSE)or Peak Signal-to-Noise Ratio(PSNR),are not always corresponding with visual observations.Structural si...It is well-known that classical quality measures,such as Mean Squared Error(MSE),Weighted Mean Squared Error(WMSE)or Peak Signal-to-Noise Ratio(PSNR),are not always corresponding with visual observations.Structural similarity based image quality assessment was proposed under the assumption that the Human Visual System(HVS)is highly adapted for extracting structural information from an image.While the demand on high color quality increases in the media industry,color loss will make the visual quality different.In this paper,we proposed an improved quality assessment(QA)method by adding color comparison into the structural similarity(SSIM)measurement system for evaluating color image quality.Then we divided the task of similarity measurement into four comparisons:luminance,contrast,structure,and color.Experimental results show that the predicted quality scores of the proposed method are more effective and consistent with visual quality than the classical methods using five different distortion types of color image sets.展开更多
The recent advancements in the field of Virtual Reality(VR)and Augmented Reality(AR)have a substantial impact on modern day technology by digitizing each and everything related to human life and open the doors to the ...The recent advancements in the field of Virtual Reality(VR)and Augmented Reality(AR)have a substantial impact on modern day technology by digitizing each and everything related to human life and open the doors to the next generation Software Technology(Soft Tech).VR and AR technology provide astonishing immersive contents with the help of high quality stitched panoramic contents and 360°imagery that widely used in the education,gaming,entertainment,and production sector.The immersive quality of VR and AR contents are greatly dependent on the perceptual quality of panoramic or 360°images,in fact a minor visual distortion can significantly degrade the overall quality.Thus,to ensure the quality of constructed panoramic contents for VR and AR applications,numerous Stitched Image Quality Assessment(SIQA)methods have been proposed to assess the quality of panoramic contents before using in VR and AR.In this survey,we provide a detailed overview of the SIQA literature and exclusively focus on objective SIQA methods presented till date.For better understanding,the objective SIQA methods are classified into two classes namely Full-Reference SIQA and No-Reference SIQA approaches.Each class is further categorized into traditional and deep learning-based methods and examined their performance for SIQA task.Further,we shortlist the publicly available benchmark SIQA datasets and evaluation metrices used for quality assessment of panoramic contents.In last,we highlight the current challenges in this area based on the existing SIQA methods and suggest future research directions that need to be target for further improvement in SIQA domain.展开更多
文摘In recent years,deep learning has been introduced into the field of Single-pixel imaging(SPI),garnering significant attention.However,conventional networks still exhibit limitations in preserving image details.To address this issue,we integrate Large Kernel Convolution(LKconv)into the U-Net framework,proposing an enhanced network structure named U-LKconv network,which significantly enhances the capability to recover image details even under low sampling conditions.
基金supported by the National Natural Science Foundation of China(No.61201425)the Natural Science Foundation of Jinagsu Province(No.BK20131280)the Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘The energy of light exposed on human skin is compulsively limited for safety reasons which affects the power of photoacoustic （PA） signal and its signal-to-noise ratio （SNR） level. Thus, the final reconstructed PA image quality is degraded. This Letter proposes an adaptive multi-sample-based approach to enhance the SNR of PA signals and in addition, detailed information in rebuilt PA images that used to be buried in the noise can be distinguished. Both ex vivo and in vivo experiments are conducted to validate the effectiveness of our proposed method which provides its potential value in clinical trials.
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 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.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National Natural Science Foundation of China(82001814)National High Level Hospital Clinical Research Funding(grant number 2022-PUMCH-B-067)+1 种基金National High Level Hospital Clinical Research Funding(grant number 2022-PUMCH-B-068)2021 SKY Imaging Research Fund of the Chinese Internatinal Medical Foundation(Z-2014-07-2101).
文摘Purpose:To assess the clinical efficacy of integrating deep learning reconstruction(DLR)with contrast-enhancement-boost(CE-boost)in 80 kVp head and neck CT angiography(CTA)using substantially lowered radiation and contrast medium(CM)doses,compared to the standard 100 kVp protocol using hybrid iterative reconstruction(HIR).Methods:Sixty-six patients were prospectively enrolled and randomly assigned to one of two groups:the low-dose group(n=33),receiving 80 kVp and 28 mL contrast medium(CM)with a noise index(NI)of 15;and the regular-dose group(n=33),receiving 100 kVp and 40 mL CM with an NI of 10.For the lowdose group,images underwent reconstruction using both hybrid iterative reconstruction(HIR)and deep learning reconstruction(DLR)at mild-,standard-,and strong-strength levels,both before and after combination with contrast enhancement-boost(CE-boost).This generated eight distinct datasets:L-HIR,L-DLR_(mild),L-DLR_(standard),L-DLR_(strong),L-HIR-CE,L-DLR_(mild)-CE,L-DLR_(standard)-CE,and L-DLR_(strong)-CE.Images for the regular-dose group were reconstructed solely with HIR(R-HIR).Quantitative analysis involved calculating and comparing CT attenuation,image noise,signal-to-noise ratio(SNR),and contrast-to-noise ratio(CNR)within six key vessels:the aortic arch(AA),internal carotid artery(ICA),external carotid artery(ECA),vertebral arteries(VA),basilar artery(BA),and middle cerebral artery(MCA).Two radiologists independently assessed subjective image quality using a 5-point scale,with statistical significance defined as P<0.05.Results:Compared to the regular-dose group,the low-dose protocol achieved a substantial reduction in contrast media volume(28 mL versus 40 mL,a 30%decrease)and radiation exposure((0.41±0.08)mSv versus(1.18±0.12)mSv,a 65%reduction).Both L-DLR_(standard) and L-DLR_(strong) delivered comparable or superior SNR and CNR across all vascular segments relative to R-HIR.However,subjective image quality scores for L-DLR at all strength levels fell below those for R-HIR(all P<0.05 for both readers).Combining CE-boost with the low-dose protocol significantly enhanced the objective image performance of L-DLR_(strong)-CE(all P<0.05)and produced subjective image scores comparable to R-HIR(reader 1:P=0.15;reader 2:P=0.06).Conclusion:When compared to the standard 100 kVp head and neck CTA,the combination of the DLR and CE-boost techniques at 80 kVp can achieve a 30%reduction in contrast dose and a 65%reduction in radiation dose,while maintaining both objective and subjective image quality.
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 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(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Project supported by the National Natural Science Foundation of China(Grant Nos.61771067,61631014,61471051,and 61401036)the Youth Research and Innovation Program of Beijing University of Posts and Telecommunications,China(Grant Nos.2015RC12 and 2017RC10)
文摘The coherent-mode representation theory is firstly used to analyze lensless two-color ghost imaging. A quite complicated expression about the point-spread function（PSF） needs to be given to analyze which wavelength has a stronger affect on imaging quality when the usual integral representation theory is used to ghost imaging. Unlike this theory, the coherent-mode representation theory shows that imaging quality depends crucially on the distribution of the decomposition coefficients of the object imaged in a two-color ghost imaging. The analytical expression of the decomposition coefficients of the object is unconcerned with the wavelength of the light used in the reference arm, but has relevance with the wavelength in the object arm. In other words, imaging quality of two-color ghost imaging depends primarily on the wavelength of the light illuminating the object. Our simulation results also demonstrate this conclusion.
基金The National Natural Science Foundation of China(No.81272501)the National Basic Research Program of China(973Program)(No.2011CB707904)Taishan Scholars Program of Shandong Province,China(No.ts20120505)
文摘To further explore the human visual system（ HVS）,the perceptual grouping（ PG）, which has been proven to play an important role in the HVS, is adopted to design an effective image quality assessment（ IQA） model. Compared with the existing fixed-window-based models, the proposed one is an adaptive window-like model that introduces the perceptual grouping strategy into the IQA model. It works as follows： first,it preprocesses the images by clustering similar pixels into a group to the greatest extent; then the structural similarity is used to compute the similarity of the superpixels between reference and distorted images; finally, it integrates all the similarity of superpixels of an image to yield a quality score. Experimental results on three databases（ LIVE, IVC and MICT） showthat the proposed method yields good performance in terms of correlation with human judgments of visual quality.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61601442,61605218,and 61575207)the National Key Research and Development Program of China(Grant No.2018YFB0504302)the Youth Innovation Promotion Association of the Chinese Academy of Sciences(Grant Nos.2015124 and 2019154)。
文摘Imaging quality is a critical component of compressive imaging in real applications. In this study, we propose a compressive imaging method based on multi-scale modulation and reconstruction in the spatial frequency domain. Theoretical analysis and simulation show the relation between the measurement matrix resolution and compressive sensing(CS)imaging quality. The matrix design is improved to provide multi-scale modulations, followed by individual reconstruction of images of different spatial frequencies. Compared with traditional single-scale CS imaging, the multi-scale method provides high quality imaging in both high and low frequencies, and effectively decreases the overall reconstruction error.Experimental results confirm the feasibility of this technique, especially at low sampling rate. The method may thus be helpful in promoting the implementation of compressive imaging in real applications.
文摘AIM: To compare the effect of oral erythromycin vs no preparation with prokinetics on the transit time and the image quality of capsule endoscopy (CE) in evaluating small bowel (SB) pathology. METHODS: We conducted a retrospective, blinded (to the type of preparation) review of 100 CE studies, 50 with no preparation with prokinetics from one medical center (Group A) and 50 from another center with administration of a single dose of 200 mg oral erythromycin 1 h prior to CE (Group B). Gastric, SB and total transit times were calculated, the presence of bile in the duodenum was scored, as was cleanliness within the proximal, middle and distal intestine. RESULTS: The erythromycin group had a slightly shorter gastric transit time (21 min vs 28 min, with no statistical significance). SB transit time was similar for both groups (all P > 0.05). Total transit time was almost identical in both groups. The rate of incomplete examination was 16% for Group A and 10% for Group B (P = 0.37). Bile and cleanliness scores in different parts of the intestine were similar for the two groups (P > 0.05). CONCLUSION: Preparation for capsule endoscopy with erythromycin does not affect SB or total transit time. It tends to reduce gastric transit time, but it does not increase the cecum-reaching rate. Erythromycin does not adversely affect image quality. We consider the routine use of oral erythromycin preparation as being unjustified, although it might be considered in patients with known prolonged gastric emptying time.
基金supported by the National Natural Science Foundation of China(Nos.62031020 and 61771279)。
文摘Neural network methods have recently emerged as a hot topic in computed tomography(CT) imaging owing to their powerful fitting ability;however, their potential applications still need to be carefully studied because their results are often difficult to interpret and are ambiguous in generalizability. Thus, quality assessments of the results obtained from a neural network are necessary to evaluate the neural network. Assessing the image quality of neural networks using traditional objective measurements is not appropriate because neural networks are nonstationary and nonlinear. In contrast, subjective assessments are trustworthy, although they are time-and energy-consuming for radiologists. Model observers that mimic subjective assessment require the mean and covariance of images, which are calculated from numerous image samples;however, this has not yet been applied to the evaluation of neural networks. In this study, we propose an analytical method for noise propagation from a single projection to efficiently evaluate convolutional neural networks(CNNs) in the CT imaging field. We propagate noise through nonlinear layers in a CNN using the Taylor expansion. Nesting of the linear and nonlinear layer noise propagation constitutes the covariance estimation of the CNN. A commonly used U-net structure is adopted for validation. The results reveal that the covariance estimation obtained from the proposed analytical method agrees well with that obtained from the image samples for different phantoms, noise levels, and activation functions, demonstrating that propagating noise from only a single projection is feasible for CNN methods in CT reconstruction. In addition, we use covariance estimation to provide three measurements for the qualitative and quantitative performance evaluation of U-net. The results indicate that the network cannot be applied to projections with high noise levels and possesses limitations in terms of efficiency for processing low-noise projections. U-net is more effective in improving the image quality of smooth regions compared with that of the edge. LeakyReLU outperforms Swish in terms of noise reduction.
文摘Objective To retrospectively evaluate the effects of saline administration following contrast material injection, abdominal compression and two delay phase acquisition on image quality improvement of computed tomographic urography (CTU). Methods Medical records and informed consents of patients were obtained. In totally 122 patients (50 men, 72 women), two delay phase images with CTU were performed. Scans began simultaneously with a contrast bolus injection of 100 mL (300 mgI/mL) followed by a saline bolus injection of 100 mL at a rate of 5 mL/s. Two delay phase images were taken at 400 and 550 seconds for each patient. Examinations were taken by using abdominal compression or not. The distention and opacification of the urinary tract were evaluated by two interpreters together on transverse images and post-processing images. Effects of four techniques (saline administration and abdominal compression, saline administration only, compression only, and neither saline administration nor compression) and two delay phase acquisition on image quality improvement were analysed by using ANOVA and Chi-square test. Results Saline administration improved opacification (P<0.05) and increased overall image quality (P<0.01) of the intrarenal collecting system and proximal ureter. Abdominal compression (P<0.05) and delayed phase image acquisition of 550 seconds (P<0.01) all improved distention of the intrarenal collecting system and proximal ureter but did not improve opacification. No statistically significant effects on the distal ureter were found. However, there were more visualized distal ureteral segments with the longer imaging delay. Conclusion Saline administration, abdominal compression and longer imaging delays are all effective in improving image quality of 64-detector row CTU.
基金the National Natural Science Foundation of China (No. 19675005).
文摘Multi-modality medical image fusion has more and more important applications in medical image analysis and understanding. In this paper, we develop and apply a multi-resolution method based on wavelet pyramid to fuse medical images from different modalities such as PET-MRI and CT-MRI. In particular, we evaluate the different fusion results when applying different selection rules and obtain optimum combination of fusion parameters.
基金Sponsored by the National Natural Science Foundation of China(Grant No.60971095 and No.61172109)Artificial Intelligence Key Laboratory of Sichuan Province(Grant No.2012RZJ01)the Fundamental Research Funds for the Central Universities(Grant No.DUT13RC201)
文摘In this paper,a new approach is proposed to determine whether the content of an image is authentic or modified with a focus on detecting complex image tampering.Detecting image tampering without any prior information of the original image is a challenging problem since unknown diverse manipulations may have different characteristics and so do various formats of images.Our principle is that image processing,no matter how complex,may affect image quality,so image quality metrics can be used to distinguish tampered images.In particular,based on the alteration of image quality in modified blocks,the proposed method can locate the tampered areas.Referring to four types of effective no-reference image quality metrics,we obtain 13 features to present an image.The experimental results show that the proposed method is very promising on detecting image tampering and locating the locally tampered areas especially in realistic scenarios.
基金supported by the Public Welfare Technology Application Research Project of Zhejiang Province,China(No.LGF21F010001)the Key Research and Development Program of Zhejiang Province,China(Grant No.2019C01002)the Key Research and Development Program of Zhejiang Province,China(Grant No.2021C03138)。
文摘Blind image quality assessment(BIQA)is of fundamental importance in low-level computer vision community.Increasing interest has been drawn in exploiting deep neural networks for BIQA.Despite of the notable success achieved,there is a broad consensus that training deep convolutional neural networks(DCNN)heavily relies on massive annotated data.Unfortunately,BIQA is typically a small sample problem,resulting the generalization ability of BIQA severely restricted.In order to improve the accuracy and generalization ability of BIQA metrics,this work proposed a totally opinion-unaware BIQA in which no subjective annotations are involved in the training stage.Multiple full-reference image quality assessment(FR-IQA)metrics are employed to label the distorted image as a substitution of subjective quality annotation.A deep neural network(DNN)is trained to blindly predict the multiple FR-IQA score in absence of corresponding pristine image.In the end,a selfsupervised FR-IQA score aggregator implemented by adversarial auto-encoder pools the predictions of multiple FR-IQA scores into the final quality predicting score.Even though none of subjective scores are involved in the training stage,experimental results indicate that our proposed full reference induced BIQA framework is as competitive as state-of-the-art BIQA metrics.
文摘It is well-known that classical quality measures,such as Mean Squared Error(MSE),Weighted Mean Squared Error(WMSE)or Peak Signal-to-Noise Ratio(PSNR),are not always corresponding with visual observations.Structural similarity based image quality assessment was proposed under the assumption that the Human Visual System(HVS)is highly adapted for extracting structural information from an image.While the demand on high color quality increases in the media industry,color loss will make the visual quality different.In this paper,we proposed an improved quality assessment(QA)method by adding color comparison into the structural similarity(SSIM)measurement system for evaluating color image quality.Then we divided the task of similarity measurement into four comparisons:luminance,contrast,structure,and color.Experimental results show that the predicted quality scores of the proposed method are more effective and consistent with visual quality than the classical methods using five different distortion types of color image sets.
文摘The recent advancements in the field of Virtual Reality(VR)and Augmented Reality(AR)have a substantial impact on modern day technology by digitizing each and everything related to human life and open the doors to the next generation Software Technology(Soft Tech).VR and AR technology provide astonishing immersive contents with the help of high quality stitched panoramic contents and 360°imagery that widely used in the education,gaming,entertainment,and production sector.The immersive quality of VR and AR contents are greatly dependent on the perceptual quality of panoramic or 360°images,in fact a minor visual distortion can significantly degrade the overall quality.Thus,to ensure the quality of constructed panoramic contents for VR and AR applications,numerous Stitched Image Quality Assessment(SIQA)methods have been proposed to assess the quality of panoramic contents before using in VR and AR.In this survey,we provide a detailed overview of the SIQA literature and exclusively focus on objective SIQA methods presented till date.For better understanding,the objective SIQA methods are classified into two classes namely Full-Reference SIQA and No-Reference SIQA approaches.Each class is further categorized into traditional and deep learning-based methods and examined their performance for SIQA task.Further,we shortlist the publicly available benchmark SIQA datasets and evaluation metrices used for quality assessment of panoramic contents.In last,we highlight the current challenges in this area based on the existing SIQA methods and suggest future research directions that need to be target for further improvement in SIQA domain.
