With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-...With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-consuming and labor-intensive,but they also struggle to provide consistent,high-precision detection and realtime monitoring of pavement surface defects.To overcome these limitations,we propose an Automatic Recognition of PavementDefect(ARPD)algorithm,which leverages unmanned aerial vehicle(UAV)-based aerial imagery to automate the inspection process.The ARPD framework incorporates a backbone network based on the Selective State Space Model(S3M),which is designed to capture long-range temporal dependencies.This enables effective modeling of dynamic correlations among redundant and often repetitive structures commonly found in road imagery.Furthermore,a neck structure based on Semantics and Detail Infusion(SDI)is introduced to guide cross-scale feature fusion.The SDI module enhances the integration of low-level spatial details with high-level semantic cues,thereby improving feature expressiveness and defect localization accuracy.Experimental evaluations demonstrate that theARPDalgorithm achieves a mean average precision(mAP)of 86.1%on a custom-labeled pavement defect dataset,outperforming the state-of-the-art YOLOv11 segmentation model.The algorithm also maintains strong generalization ability on public datasets.These results confirm that ARPD is well-suited for diverse real-world applications in intelligent,large-scale highway defect monitoring and maintenance planning.展开更多
Unmanned aerial vehicle(UAV)-borne gamma-ray spectrum survey plays a crucial role in geological mapping,radioactive mineral exploration,and environmental monitoring.However,raw data are often compromised by flight and...Unmanned aerial vehicle(UAV)-borne gamma-ray spectrum survey plays a crucial role in geological mapping,radioactive mineral exploration,and environmental monitoring.However,raw data are often compromised by flight and instrument background noise,as well as detector resolution limitations,which affect the accuracy of geological interpretations.This study aims to explore the application of the Real-ESRGAN algorithm in the super-resolution reconstruction of UAV-borne gamma-ray spectrum images to enhance spatial resolution and the quality of geological feature visualization.We conducted super-resolution reconstruction experiments with 2×,4×and 6×magnification using the Real-ESRGAN algorithm,comparing the results with three other mainstream algorithms(SRCNN,SRGAN,FSRCNN)to verify the superiority in image quality.The experimental results indicate that Real-ESRGAN achieved a structural similarity index(SSIM)value of 0.950 at 2×magnification,significantly higher than the other algorithms,demonstrating its advantage in detail preservation.Furthermore,Real-ESRGAN effectively reduced ringing and overshoot artifacts,enhancing the clarity of geological structures and mineral deposit sites,thus providing high-quality visual information for geological exploration.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
Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time perfor...Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time performance and monitoring scope.To address this,a temperature detection method based on infrared image processing has been proposed:utilizing the median filtering algorithm to denoise the original infrared image,then applying an image segmentation algorithm to divide the image.展开更多
Karst fractures serve as crucial seepage channels and storage spaces for carbonate natural gas reservoirs,and electrical image logs are vital data for visualizing and characterizing such fractures.However,the conventi...Karst fractures serve as crucial seepage channels and storage spaces for carbonate natural gas reservoirs,and electrical image logs are vital data for visualizing and characterizing such fractures.However,the conventional approach of identifying fractures using electrical image logs predominantly relies on manual processes that are not only time-consuming but also highly subjective.In addition,the heterogeneity and strong dissolution tendency of karst carbonate reservoirs lead to complexity and variety in fracture geometry,which makes it difficult to accurately identify fractures.In this paper,the electrical image logs network(EILnet)da deep-learning-based intelligent semantic segmentation model with a selective attention mechanism and selective feature fusion moduledwas created to enable the intelligent identification and segmentation of different types of fractures through electrical logging images.Data from electrical image logs representing structural and induced fractures were first selected using the sliding window technique before image inpainting and data augmentation were implemented for these images to improve the generalizability of the model.Various image-processing tools,including the bilateral filter,Laplace operator,and Gaussian low-pass filter,were also applied to the electrical logging images to generate a multi-attribute dataset to help the model learn the semantic features of the fractures.The results demonstrated that the EILnet model outperforms mainstream deep-learning semantic segmentation models,such as Fully Convolutional Networks(FCN-8s),U-Net,and SegNet,for both the single-channel dataset and the multi-attribute dataset.The EILnet provided significant advantages for the single-channel dataset,and its mean intersection over union(MIoU)and pixel accuracy(PA)were 81.32%and 89.37%,respectively.In the case of the multi-attribute dataset,the identification capability of all models improved to varying degrees,with the EILnet achieving the highest MIoU and PA of 83.43%and 91.11%,respectively.Further,applying the EILnet model to various blind wells demonstrated its ability to provide reliable fracture identification,thereby indicating its promising potential applications.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
Structural Health Monitoring(SHM)systems play a key role in managing buildings and infrastructure by delivering vital insights into their strength and structural integrity.There is a need for more efficient techniques...Structural Health Monitoring(SHM)systems play a key role in managing buildings and infrastructure by delivering vital insights into their strength and structural integrity.There is a need for more efficient techniques to detect defects,as traditional methods are often prone to human error,and this issue is also addressed through image processing(IP).In addition to IP,automated,accurate,and real-time detection of structural defects,such as cracks,corrosion,and material degradation that conventional inspection techniques may miss,is made possible by Artificial Intelligence(AI)technologies like Machine Learning(ML)and Deep Learning(DL).This review examines the integration of computer vision and AI techniques in Structural Health Monitoring(SHM),investigating their effectiveness in detecting various forms of structural deterioration.Also,it evaluates ML and DL models in SHM for their accuracy in identifying and assessing structural damage,ultimately enhancing safety,durability,and maintenance practices in the field.Key findings reveal that AI-powered approaches,especially those utilizing IP and DL models like CNNs,significantly improve detection efficiency and accuracy,with reported accuracies in various SHM tasks.However,significant research gaps remain,including challenges with the consistency,quality,and environmental resilience of image data,a notable lack of standardized models and datasets for training across diverse structures,and concerns regarding computational costs,model interpretability,and seamless integration with existing systems.Future work should focus on developing more robust models through data augmentation,transfer learning,and hybrid approaches,standardizing protocols,and fostering interdisciplinary collaboration to overcome these limitations and achieve more reliable,scalable,and affordable SHM systems.展开更多
Carotid artery plaques represent a major contributor to the morbidity and mortality associated with cerebrovascular disease,and their clinical significance is largely determined by the risk linked to plaque vulnerabil...Carotid artery plaques represent a major contributor to the morbidity and mortality associated with cerebrovascular disease,and their clinical significance is largely determined by the risk linked to plaque vulnerability.Therefore,classifying plaque risk constitutes one of themost critical tasks in the clinicalmanagement of this condition.While classification models derived from individual medical centers have been extensively investigated,these singlecenter models often fail to generalize well to multi-center data due to variations in ultrasound images caused by differences in physician expertise and equipment.To address this limitation,a Dual-Classifier Label Correction Networkmodel(DCLCN)is proposed for the classification of carotid plaque ultrasound images acrossmultiplemedical centers.TheDCLCNdesigns amulti-center domain adaptationmodule that leverages a dual-classifier strategy to extract knowledge from both source and target centers,thereby reducing feature discrepancies through a domain adaptation layer.Additionally,to mitigate the impact of image noise,a label modeling and correction module is introduced to generate pseudo-labels for the target centers and iteratively refine them using an end-to-end correction mechanism.Experiments on the carotid plaque dataset collected fromthreemedical centers demonstrate that the DCLCN achieves commendable performance and robustness.展开更多
Osteosarcomas are malignant neoplasms derived from undifferentiated osteogenic mesenchymal cells. It causes severe and permanent damage to human tissue and has a high mortality rate. The condition has the capacity to ...Osteosarcomas are malignant neoplasms derived from undifferentiated osteogenic mesenchymal cells. It causes severe and permanent damage to human tissue and has a high mortality rate. The condition has the capacity to occur in any bone;however, it often impacts long bones like the arms and legs. Prompt identification and prompt intervention are essential for augmenting patient longevity. However, the intricate composition and erratic placement of osteosarcoma provide difficulties for clinicians in accurately determining the scope of the afflicted area. There is a pressing requirement for developing an algorithm that can automatically detect bone tumors with tremendous accuracy. Therefore, in this study, we proposed a novel feature extractor framework associated with a supervised three-class XGBoost algorithm for the detection of osteosarcoma in whole slide histopathology images. This method allows for quicker and more effective data analysis. The first step involves preprocessing the imbalanced histopathology dataset, followed by augmentation and balancing utilizing two techniques: SMOTE and ADASYN. Next, a unique feature extraction framework is used to extract features, which are then inputted into the supervised three-class XGBoost algorithm for classification into three categories: non-tumor, viable tumor, and non-viable tumor. The experimental findings indicate that the proposed model exhibits superior efficiency, accuracy, and a more lightweight design in comparison to other current models for osteosarcoma detection.展开更多
In digital signal processing,image enhancement or image denoising are challenging task to preserve pixel quality.There are several approaches from conventional to deep learning that are used to resolve such issues.But...In digital signal processing,image enhancement or image denoising are challenging task to preserve pixel quality.There are several approaches from conventional to deep learning that are used to resolve such issues.But they still face challenges in terms of computational requirements,overfitting and generalization issues,etc.To resolve such issues,optimization algorithms provide greater control and transparency in designing digital filters for image enhancement and denoising.Therefore,this paper presented a novel denoising approach for medical applications using an Optimized Learning⁃based Multi⁃level discrete Wavelet Cascaded Convolutional Neural Network(OLMWCNN).In this approach,the optimal filter parameters are identified to preserve the image quality after denoising.The performance and efficiency of the OLMWCNN filter are evaluated,demonstrating significant progress in denoising medical images while overcoming the limitations of conventional methods.展开更多
This study seeks to establish a novel,semi-automatic system that utilizes Industry 4.0 principles to effectively determine both acceptable and rejectable concrete cubes with regard to their failure modes,significantly...This study seeks to establish a novel,semi-automatic system that utilizes Industry 4.0 principles to effectively determine both acceptable and rejectable concrete cubes with regard to their failure modes,significantly contributing to the dependability of concrete quality evaluations.The study utilizes image processing and machine learning(ML)methods,namely object detectionmodels such as YOLOv8 and Convolutional Neural Networks(CNNs),to evaluate images of concrete cubes.These models are trained and validated on an extensive database of annotated images from real-world and laboratory conditions.Preliminary results indicate a good performance in the classification of concrete cube failure modes.The proposed system accurately identifies cracks,determines the severity of damage to structures,indicating the potential to minimize human errors and discrepancies that might occur through the current techniques to detect the failure mode of concrete cubes.Thedeveloped systemcould significantly improve the reliability of concrete cube assessments,reduce resource wastage,and contribute to more sustainable construction practices.By minimizing material costs and errors,this innovation supports the construction industry’s move towards sustainability.展开更多
Rain streaks introduced by atmospheric precipitation significantly degrade image quality and impair the reliability of high-level vision tasks.We present a novel image deraining framework built on a three-stage dual-r...Rain streaks introduced by atmospheric precipitation significantly degrade image quality and impair the reliability of high-level vision tasks.We present a novel image deraining framework built on a three-stage dual-residual architecture that progressively restores rain-degraded content while preserving fine structural details.Each stage begins with a multi-scale feature extractor and a channel attention module that adaptively emphasizes informative representations for rain removal.The core restoration is achieved via enhanced dual-residual blocks,which stabilize training and mitigate feature degradation across layers.To further refine representations,we integrate crossdimensional spatial attention supervised by ground-truth guidance,ensuring that only high-quality features propagate to subsequent stages.Inter-stage feature fusion modules are employed to aggregate complementary information,reinforcing reconstruction continuity and consistency.Extensive experiments on five benchmark datasets(Rain100H,Rain100L,RainKITTI2012,RainKITTI2015,and JRSRD)demonstrate that our method establishes new state-of-the-art results in both fidelity and perceptual quality,effectively removing rain streaks while preserving natural textures and structural integrity.展开更多
Hematoxylin and Eosin(H&E)images,popularly used in the field of digital pathology,often pose challenges due to their limited color richness,hindering the differentiation of subtle cell features crucial for accurat...Hematoxylin and Eosin(H&E)images,popularly used in the field of digital pathology,often pose challenges due to their limited color richness,hindering the differentiation of subtle cell features crucial for accurate classification.Enhancing the visibility of these elusive cell features helps train robust deep-learning models.However,the selection and application of image processing techniques for such enhancement have not been systematically explored in the research community.To address this challenge,we introduce Salient Features Guided Augmentation(SFGA),an approach that strategically integrates machine learning and image processing.SFGA utilizes machine learning algorithms to identify crucial features within cell images,subsequently mapping these features to appropriate image processing techniques to enhance training images.By emphasizing salient features and aligning them with corresponding image processing methods,SFGA is designed to enhance the discriminating power of deep learning models in cell classification tasks.Our research undertakes a series of experiments,each exploring the performance of different datasets and data enhancement techniques in classifying cell types,highlighting the significance of data quality and enhancement in mitigating overfitting and distinguishing cell characteristics.Specifically,SFGA focuses on identifying tumor cells from tissue for extranodal extension detection,with the SFGA-enhanced dataset showing notable advantages in accuracy.We conducted a preliminary study of five experiments,among which the accuracy of the pleomorphism experiment improved significantly from 50.81%to 95.15%.The accuracy of the other four experiments also increased,with improvements ranging from 3 to 43 percentage points.Our preliminary study shows the possibilities to enhance the diagnostic accuracy of deep learning models and proposes a systematic approach that could enhance cancer diagnosis,contributing as a first step in using SFGA in medical image enhancement.展开更多
Low-light images often have defects such as low visibility,low contrast,high noise,and high color distortion compared with well-exposed images.If the low-light region of an image is enhanced directly,the noise will in...Low-light images often have defects such as low visibility,low contrast,high noise,and high color distortion compared with well-exposed images.If the low-light region of an image is enhanced directly,the noise will inevitably blur the whole image.Besides,according to the retina-and-cortex(retinex)theory of color vision,the reflectivity of different image regions may differ,limiting the enhancement performance of applying uniform operations to the entire image.Therefore,we design a Hierarchical Flow Learning(HFL)framework,which consists of a Hierarchical Image Network(HIN)and a normalized invertible Flow Learning Network(FLN).HIN can extract hierarchical structural features from low-light images,while FLN maps the distribution of normally exposed images to a Gaussian distribution using the learned hierarchical features of low-light images.In subsequent testing,the reversibility of FLN allows inferring and obtaining enhanced low-light images.Specifically,the HIN extracts as much image information as possible from three scales,local,regional,and global,using a Triple-branch Hierarchical Fusion Module(THFM)and a Dual-Dconv Cross Fusion Module(DCFM).The THFM aggregates regional and global features to enhance the overall brightness and quality of low-light images by perceiving and extracting more structure information,whereas the DCFM uses the properties of the activation function and local features to enhance images at the pixel-level to reduce noise and improve contrast.In addition,in this paper,the model was trained using a negative log-likelihood loss function.Qualitative and quantitative experimental results demonstrate that our HFL can better handle many quality degradation types in low-light images compared with state-of-the-art solutions.The HFL model enhances low-light images with better visibility,less noise,and improved contrast,suitable for practical scenarios such as autonomous driving,medical imaging,and nighttime surveillance.Outperforming them by PSNR=27.26 dB,SSIM=0.93,and LPIPS=0.10 on benchmark dataset LOL-v1.The source code of HFL is available at https://github.com/Smile-QT/HFL-for-LIE.展开更多
In complex industrial scenes,it is difficult to acquire high-precision non-cooperative target pose under monocular visual servo control.This paper presents a new method of target extraction and high-precision edge fit...In complex industrial scenes,it is difficult to acquire high-precision non-cooperative target pose under monocular visual servo control.This paper presents a new method of target extraction and high-precision edge fitting for the wheel of the sintering trolley in steel production,which fuses multiple target extraction algorithms adapting to the working environment of the target.Firstly,based on obvious difference between the pixels of the target image and the non-target image in the gray histogram,these pixels were classified and then segmented in intraclass,removing interference factors and remaining the target image.Then,multiple segmentation results were merged and a final target image was obtained after small connected regions were eliminated.In the edge fitting stage,the edge fitting method with best-circumscribed rectangle was proposed to accurately fit the circular target edge.Finally,PnP algorithm was adopted for pose measurement of the target.The experimental results showed that the average estimation error of pose angleγwith respect to the z-axis rotation was 0.2346°,the average measurement error of pose angleαwith respect to the x-axis rotation was 0.1703°,and the average measurement error of pose angle β with respect to the y-axis rotation was 0.2275°.The proposed method has practical application value.展开更多
With rapid urbanization,fires pose significant challenges in urban governance.Traditional fire detection methods often struggle to detect smoke in complex urban scenes due to environmental interferences and variations...With rapid urbanization,fires pose significant challenges in urban governance.Traditional fire detection methods often struggle to detect smoke in complex urban scenes due to environmental interferences and variations in viewing angles.This study proposes a novel multimodal smoke detection method that fuses infrared and visible imagery using a transformer-based deep learning model.By capturing both thermal and visual cues,our approach significantly enhances the accuracy and robustness of smoke detection in business parks scenes.We first established a dual-view dataset comprising infrared and visible light videos,implemented an innovative image feature fusion strategy,and designed a deep learning model based on the transformer architecture and attention mechanism for smoke classification.Experimental results demonstrate that our method outperforms existing methods,under the condition of multi-view input,it achieves an accuracy rate of 90.88%,precision rate of 98.38%,recall rate of 92.41%and false positive and false negative rates both below 5%,underlining the effectiveness of the proposed multimodal and multi-view fusion approach.The attention mechanism plays a crucial role in improving detection performance,particularly in identifying subtle smoke features.展开更多
Terahertz imaging technology has great potential applications in areas,such as remote sensing,navigation,security checks,and so on.However,terahertz images usually have the problems of heavy noises and low resolution....Terahertz imaging technology has great potential applications in areas,such as remote sensing,navigation,security checks,and so on.However,terahertz images usually have the problems of heavy noises and low resolution.Previous terahertz image denoising methods are mainly based on traditional image processing methods,which have limited denoising effects on the terahertz noise.Existing deep learning-based image denoising methods are mostly used in natural images and easily cause a large amount of detail loss when denoising terahertz images.Here,a residual-learning-based multiscale hybridconvolution residual network(MHRNet)is proposed for terahertz image denoising,which can remove noises while preserving detail features in terahertz images.Specifically,a multiscale hybrid-convolution residual block(MHRB)is designed to extract rich detail features and local prediction residual noise from terahertz images.Specifically,MHRB is a residual structure composed of a multiscale dilated convolution block,a bottleneck layer,and a multiscale convolution block.MHRNet uses the MHRB and global residual learning to achieve terahertz image denoising.Ablation studies are performed to validate the effectiveness of MHRB.A series of experiments are conducted on the public terahertz image datasets.The experimental results demonstrate that MHRNet has an excellent denoising effect on synthetic and real noisy terahertz images.Compared with existing methods,MHRNet achieves comprehensive competitive results.展开更多
In micro milling machining,tool wear directly affects workpiece quality and accuracy,making effective tool wear monitoring a key factor in ensuring product integrity.The use of machine vision-based methods can provide...In micro milling machining,tool wear directly affects workpiece quality and accuracy,making effective tool wear monitoring a key factor in ensuring product integrity.The use of machine vision-based methods can provide an intuitive and efficient representation of tool wear conditions.However,micro milling tools have non-flat flanks,thin coatings can peel off,and spindle orientation is uncertain during downtime.These factors result in low pixel values,uneven illumination,and arbitrary tool position.To address this,we propose an image-based tool wear monitoring method.It combines multiple algorithms to restore lost pixels due to uneven illumination during segmentation and accurately extract wear areas.Experimental results demonstrate that the proposed algorithm exhibits high robustness to such images,effectively addressing the effects of illumination and spindle orientation.Additionally,the algorithm has low complexity,fast execution time,and significantly reduces the detection time in situ.展开更多
A detector's nondestructive readout mode allows its pixels to be read multiple times during integration,enabling generation of a series of"up-the-ramp"images that continuously accumulate photons between ...A detector's nondestructive readout mode allows its pixels to be read multiple times during integration,enabling generation of a series of"up-the-ramp"images that continuously accumulate photons between successive frames.Because noise is correlated across these images,optimal stacking generally requires the images to be weighted unequally to achieve the best possible target signal-to-noise ratio(SNR).Objects in the sky present wildly varied brightness characteristics,and the counts in individual pixels of the same object can also span wide ranges.Therefore,a single set of weights cannot be optimal in all cases.To ensure that the stacked image is easily calibratable,we apply the same weight to all pixels within the same frame.In practice,results for high-SNR cases degraded only slightly when we used weights derived for low-SNR cases,whereas the low-SNR cases remained more sensitive to the weights.Therefore,we propose a quasi-optimal stacking method that maximizes the stacked SNR for the case where the RSN=1 per pixel in the last frame and use simulated data to demonstrate that this approach enhances the SNR more strongly than the equal-weight stacking and ramp fitting methods.Furthermore,we estimate the improvements in the limiting magnitudes for the China Space Station Telescope using the proposed method.When compared with the conventional readout mode,which is equivalent to selecting the last frame from the nondestructive readout,stacking 30 up-the-ramp images can improve the limiting magnitude by approximately 0.5 mag for the telescope's near-infrared observations,effectively reducing readout noise by approximately 62%.展开更多
The internal microstructures of rock materials, including mineral heterogeneity and intrinsic microdefects, exert a significant influence on their nonlinear mechanical and cracking behaviors. It is of great significan...The internal microstructures of rock materials, including mineral heterogeneity and intrinsic microdefects, exert a significant influence on their nonlinear mechanical and cracking behaviors. It is of great significance to accurately characterize the actual microstructures and their influence on stress and damage evolution inside the rocks. In this study, an image-based fast Fourier transform (FFT) method is developed for reconstructing the actual rock microstructures by combining it with the digital image processing (DIP) technique. A series of experimental investigations were conducted to acquire information regarding the actual microstructure and the mechanical properties. Based on these experimental evidences, the processed microstructure information, in conjunction with the proposed micromechanical model, is incorporated into the numerical calculation. The proposed image-based FFT method was firstly validated through uniaxial compression tests. Subsequently, it was employed to predict and analyze the influence of microstructure on macroscopic mechanical behaviors, local stress distribution and the internal crack evolution process in brittle rocks. The distribution of feldspar is considerably more heterogeneous and scattered than that of quartz, which results in a greater propensity for the formation of cracks in feldspar. It is observed that initial cracks and new cracks, including intragranular and boundary ones, ultimately coalesce and connect as the primary through cracks, which are predominantly distributed along the boundary of the feldspar. This phenomenon is also predicted by the proposed numerical method. The results indicate that the proposed numerical method provides an effective approach for analyzing, understanding and predicting the nonlinear mechanical and cracking behaviors of brittle rocks by taking into account the actual microstructure characteristics.展开更多
基金supported in part by the Technical Service for the Development and Application of an Intelligent Visual Management Platformfor Expressway Construction Progress Based on BIM Technology(grant NO.JKYZLX-2023-09)in partby the Technical Service for the Development of an Early Warning Model in the Research and Application of Key Technologies for Tunnel Operation Safety Monitoring and Early Warning Based on Digital Twin(grant NO.JK-S02-ZNGS-202412-JISHU-FA-0035)sponsored by Yunnan Transportation Science Research Institute Co.,Ltd.
文摘With the rapid development of transportation infrastructure,ensuring road safety through timely and accurate highway inspection has become increasingly critical.Traditional manual inspection methods are not only time-consuming and labor-intensive,but they also struggle to provide consistent,high-precision detection and realtime monitoring of pavement surface defects.To overcome these limitations,we propose an Automatic Recognition of PavementDefect(ARPD)algorithm,which leverages unmanned aerial vehicle(UAV)-based aerial imagery to automate the inspection process.The ARPD framework incorporates a backbone network based on the Selective State Space Model(S3M),which is designed to capture long-range temporal dependencies.This enables effective modeling of dynamic correlations among redundant and often repetitive structures commonly found in road imagery.Furthermore,a neck structure based on Semantics and Detail Infusion(SDI)is introduced to guide cross-scale feature fusion.The SDI module enhances the integration of low-level spatial details with high-level semantic cues,thereby improving feature expressiveness and defect localization accuracy.Experimental evaluations demonstrate that theARPDalgorithm achieves a mean average precision(mAP)of 86.1%on a custom-labeled pavement defect dataset,outperforming the state-of-the-art YOLOv11 segmentation model.The algorithm also maintains strong generalization ability on public datasets.These results confirm that ARPD is well-suited for diverse real-world applications in intelligent,large-scale highway defect monitoring and maintenance planning.
基金supported by the National Natural Science Foundation of China(Nos.12205044 and 12265003)2024 Jiangxi Province Civil-Military Integration Research Institute‘BeiDou+’Project Subtopic(No.2024JXRH0Y06).
文摘Unmanned aerial vehicle(UAV)-borne gamma-ray spectrum survey plays a crucial role in geological mapping,radioactive mineral exploration,and environmental monitoring.However,raw data are often compromised by flight and instrument background noise,as well as detector resolution limitations,which affect the accuracy of geological interpretations.This study aims to explore the application of the Real-ESRGAN algorithm in the super-resolution reconstruction of UAV-borne gamma-ray spectrum images to enhance spatial resolution and the quality of geological feature visualization.We conducted super-resolution reconstruction experiments with 2×,4×and 6×magnification using the Real-ESRGAN algorithm,comparing the results with three other mainstream algorithms(SRCNN,SRGAN,FSRCNN)to verify the superiority in image quality.The experimental results indicate that Real-ESRGAN achieved a structural similarity index(SSIM)value of 0.950 at 2×magnification,significantly higher than the other algorithms,demonstrating its advantage in detail preservation.Furthermore,Real-ESRGAN effectively reduced ringing and overshoot artifacts,enhancing the clarity of geological structures and mineral deposit sites,thus providing high-quality visual information for geological exploration.
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 Key Research and Development Project of China(No.2023YFB3709605)the National Natural Science Foundation of China(No.62073193)the National College Student Innovation Training Program(No.202310422122)。
文摘Potential high-temperature risks exist in heat-prone components of electric moped charging devices,such as sockets,interfaces,and controllers.Traditional detection methods have limitations in terms of real-time performance and monitoring scope.To address this,a temperature detection method based on infrared image processing has been proposed:utilizing the median filtering algorithm to denoise the original infrared image,then applying an image segmentation algorithm to divide the image.
基金the National Natural Science Foundation of China(42472194,42302153,and 42002144)the Fundamental Research Funds for the Central Univer-sities(22CX06002A).
文摘Karst fractures serve as crucial seepage channels and storage spaces for carbonate natural gas reservoirs,and electrical image logs are vital data for visualizing and characterizing such fractures.However,the conventional approach of identifying fractures using electrical image logs predominantly relies on manual processes that are not only time-consuming but also highly subjective.In addition,the heterogeneity and strong dissolution tendency of karst carbonate reservoirs lead to complexity and variety in fracture geometry,which makes it difficult to accurately identify fractures.In this paper,the electrical image logs network(EILnet)da deep-learning-based intelligent semantic segmentation model with a selective attention mechanism and selective feature fusion moduledwas created to enable the intelligent identification and segmentation of different types of fractures through electrical logging images.Data from electrical image logs representing structural and induced fractures were first selected using the sliding window technique before image inpainting and data augmentation were implemented for these images to improve the generalizability of the model.Various image-processing tools,including the bilateral filter,Laplace operator,and Gaussian low-pass filter,were also applied to the electrical logging images to generate a multi-attribute dataset to help the model learn the semantic features of the fractures.The results demonstrated that the EILnet model outperforms mainstream deep-learning semantic segmentation models,such as Fully Convolutional Networks(FCN-8s),U-Net,and SegNet,for both the single-channel dataset and the multi-attribute dataset.The EILnet provided significant advantages for the single-channel dataset,and its mean intersection over union(MIoU)and pixel accuracy(PA)were 81.32%and 89.37%,respectively.In the case of the multi-attribute dataset,the identification capability of all models improved to varying degrees,with the EILnet achieving the highest MIoU and PA of 83.43%and 91.11%,respectively.Further,applying the EILnet model to various blind wells demonstrated its ability to provide reliable fracture identification,thereby indicating its promising potential applications.
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.
文摘Structural Health Monitoring(SHM)systems play a key role in managing buildings and infrastructure by delivering vital insights into their strength and structural integrity.There is a need for more efficient techniques to detect defects,as traditional methods are often prone to human error,and this issue is also addressed through image processing(IP).In addition to IP,automated,accurate,and real-time detection of structural defects,such as cracks,corrosion,and material degradation that conventional inspection techniques may miss,is made possible by Artificial Intelligence(AI)technologies like Machine Learning(ML)and Deep Learning(DL).This review examines the integration of computer vision and AI techniques in Structural Health Monitoring(SHM),investigating their effectiveness in detecting various forms of structural deterioration.Also,it evaluates ML and DL models in SHM for their accuracy in identifying and assessing structural damage,ultimately enhancing safety,durability,and maintenance practices in the field.Key findings reveal that AI-powered approaches,especially those utilizing IP and DL models like CNNs,significantly improve detection efficiency and accuracy,with reported accuracies in various SHM tasks.However,significant research gaps remain,including challenges with the consistency,quality,and environmental resilience of image data,a notable lack of standardized models and datasets for training across diverse structures,and concerns regarding computational costs,model interpretability,and seamless integration with existing systems.Future work should focus on developing more robust models through data augmentation,transfer learning,and hybrid approaches,standardizing protocols,and fostering interdisciplinary collaboration to overcome these limitations and achieve more reliable,scalable,and affordable SHM systems.
基金supported by Shanghai Technical Service Computing Center of Science and Engineering,Shanghai University.
文摘Carotid artery plaques represent a major contributor to the morbidity and mortality associated with cerebrovascular disease,and their clinical significance is largely determined by the risk linked to plaque vulnerability.Therefore,classifying plaque risk constitutes one of themost critical tasks in the clinicalmanagement of this condition.While classification models derived from individual medical centers have been extensively investigated,these singlecenter models often fail to generalize well to multi-center data due to variations in ultrasound images caused by differences in physician expertise and equipment.To address this limitation,a Dual-Classifier Label Correction Networkmodel(DCLCN)is proposed for the classification of carotid plaque ultrasound images acrossmultiplemedical centers.TheDCLCNdesigns amulti-center domain adaptationmodule that leverages a dual-classifier strategy to extract knowledge from both source and target centers,thereby reducing feature discrepancies through a domain adaptation layer.Additionally,to mitigate the impact of image noise,a label modeling and correction module is introduced to generate pseudo-labels for the target centers and iteratively refine them using an end-to-end correction mechanism.Experiments on the carotid plaque dataset collected fromthreemedical centers demonstrate that the DCLCN achieves commendable performance and robustness.
文摘Osteosarcomas are malignant neoplasms derived from undifferentiated osteogenic mesenchymal cells. It causes severe and permanent damage to human tissue and has a high mortality rate. The condition has the capacity to occur in any bone;however, it often impacts long bones like the arms and legs. Prompt identification and prompt intervention are essential for augmenting patient longevity. However, the intricate composition and erratic placement of osteosarcoma provide difficulties for clinicians in accurately determining the scope of the afflicted area. There is a pressing requirement for developing an algorithm that can automatically detect bone tumors with tremendous accuracy. Therefore, in this study, we proposed a novel feature extractor framework associated with a supervised three-class XGBoost algorithm for the detection of osteosarcoma in whole slide histopathology images. This method allows for quicker and more effective data analysis. The first step involves preprocessing the imbalanced histopathology dataset, followed by augmentation and balancing utilizing two techniques: SMOTE and ADASYN. Next, a unique feature extraction framework is used to extract features, which are then inputted into the supervised three-class XGBoost algorithm for classification into three categories: non-tumor, viable tumor, and non-viable tumor. The experimental findings indicate that the proposed model exhibits superior efficiency, accuracy, and a more lightweight design in comparison to other current models for osteosarcoma detection.
文摘In digital signal processing,image enhancement or image denoising are challenging task to preserve pixel quality.There are several approaches from conventional to deep learning that are used to resolve such issues.But they still face challenges in terms of computational requirements,overfitting and generalization issues,etc.To resolve such issues,optimization algorithms provide greater control and transparency in designing digital filters for image enhancement and denoising.Therefore,this paper presented a novel denoising approach for medical applications using an Optimized Learning⁃based Multi⁃level discrete Wavelet Cascaded Convolutional Neural Network(OLMWCNN).In this approach,the optimal filter parameters are identified to preserve the image quality after denoising.The performance and efficiency of the OLMWCNN filter are evaluated,demonstrating significant progress in denoising medical images while overcoming the limitations of conventional methods.
文摘This study seeks to establish a novel,semi-automatic system that utilizes Industry 4.0 principles to effectively determine both acceptable and rejectable concrete cubes with regard to their failure modes,significantly contributing to the dependability of concrete quality evaluations.The study utilizes image processing and machine learning(ML)methods,namely object detectionmodels such as YOLOv8 and Convolutional Neural Networks(CNNs),to evaluate images of concrete cubes.These models are trained and validated on an extensive database of annotated images from real-world and laboratory conditions.Preliminary results indicate a good performance in the classification of concrete cube failure modes.The proposed system accurately identifies cracks,determines the severity of damage to structures,indicating the potential to minimize human errors and discrepancies that might occur through the current techniques to detect the failure mode of concrete cubes.Thedeveloped systemcould significantly improve the reliability of concrete cube assessments,reduce resource wastage,and contribute to more sustainable construction practices.By minimizing material costs and errors,this innovation supports the construction industry’s move towards sustainability.
基金supported by Key Scientific and Technological Research Program of Henan Province(Grant No.252102211111).
文摘Rain streaks introduced by atmospheric precipitation significantly degrade image quality and impair the reliability of high-level vision tasks.We present a novel image deraining framework built on a three-stage dual-residual architecture that progressively restores rain-degraded content while preserving fine structural details.Each stage begins with a multi-scale feature extractor and a channel attention module that adaptively emphasizes informative representations for rain removal.The core restoration is achieved via enhanced dual-residual blocks,which stabilize training and mitigate feature degradation across layers.To further refine representations,we integrate crossdimensional spatial attention supervised by ground-truth guidance,ensuring that only high-quality features propagate to subsequent stages.Inter-stage feature fusion modules are employed to aggregate complementary information,reinforcing reconstruction continuity and consistency.Extensive experiments on five benchmark datasets(Rain100H,Rain100L,RainKITTI2012,RainKITTI2015,and JRSRD)demonstrate that our method establishes new state-of-the-art results in both fidelity and perceptual quality,effectively removing rain streaks while preserving natural textures and structural integrity.
基金supported by grants fromthe North China University of Technology Research Start-Up Fund(11005136024XN147-14)and(110051360024XN151-97)Guangzhou Development Zone Science and Technology Project(2023GH02)+4 种基金the National Key R&D Program of China(2021YFE0201100 and 2022YFA1103401 to Juntao Gao)National Natural Science Foundation of China(981890991 to Juntao Gao)Beijing Municipal Natural Science Foundation(Z200021 to Juntao Gao)CAS Interdisciplinary Innovation Team(JCTD-2020-04 to Juntao Gao)0032/2022/A,by Macao FDCT,and MYRG2022-00271-FST.
文摘Hematoxylin and Eosin(H&E)images,popularly used in the field of digital pathology,often pose challenges due to their limited color richness,hindering the differentiation of subtle cell features crucial for accurate classification.Enhancing the visibility of these elusive cell features helps train robust deep-learning models.However,the selection and application of image processing techniques for such enhancement have not been systematically explored in the research community.To address this challenge,we introduce Salient Features Guided Augmentation(SFGA),an approach that strategically integrates machine learning and image processing.SFGA utilizes machine learning algorithms to identify crucial features within cell images,subsequently mapping these features to appropriate image processing techniques to enhance training images.By emphasizing salient features and aligning them with corresponding image processing methods,SFGA is designed to enhance the discriminating power of deep learning models in cell classification tasks.Our research undertakes a series of experiments,each exploring the performance of different datasets and data enhancement techniques in classifying cell types,highlighting the significance of data quality and enhancement in mitigating overfitting and distinguishing cell characteristics.Specifically,SFGA focuses on identifying tumor cells from tissue for extranodal extension detection,with the SFGA-enhanced dataset showing notable advantages in accuracy.We conducted a preliminary study of five experiments,among which the accuracy of the pleomorphism experiment improved significantly from 50.81%to 95.15%.The accuracy of the other four experiments also increased,with improvements ranging from 3 to 43 percentage points.Our preliminary study shows the possibilities to enhance the diagnostic accuracy of deep learning models and proposes a systematic approach that could enhance cancer diagnosis,contributing as a first step in using SFGA in medical image enhancement.
基金supported by the National Natural Science Foundation of China(Grant Nos.61971078,61501070)the Scientific Research Foundation of Chongqing University of Technology(Grant No.0121230236)the Science and Technology Research Program of Chongqing Municipal Education Commission(Grant No.KJ202301165).
文摘Low-light images often have defects such as low visibility,low contrast,high noise,and high color distortion compared with well-exposed images.If the low-light region of an image is enhanced directly,the noise will inevitably blur the whole image.Besides,according to the retina-and-cortex(retinex)theory of color vision,the reflectivity of different image regions may differ,limiting the enhancement performance of applying uniform operations to the entire image.Therefore,we design a Hierarchical Flow Learning(HFL)framework,which consists of a Hierarchical Image Network(HIN)and a normalized invertible Flow Learning Network(FLN).HIN can extract hierarchical structural features from low-light images,while FLN maps the distribution of normally exposed images to a Gaussian distribution using the learned hierarchical features of low-light images.In subsequent testing,the reversibility of FLN allows inferring and obtaining enhanced low-light images.Specifically,the HIN extracts as much image information as possible from three scales,local,regional,and global,using a Triple-branch Hierarchical Fusion Module(THFM)and a Dual-Dconv Cross Fusion Module(DCFM).The THFM aggregates regional and global features to enhance the overall brightness and quality of low-light images by perceiving and extracting more structure information,whereas the DCFM uses the properties of the activation function and local features to enhance images at the pixel-level to reduce noise and improve contrast.In addition,in this paper,the model was trained using a negative log-likelihood loss function.Qualitative and quantitative experimental results demonstrate that our HFL can better handle many quality degradation types in low-light images compared with state-of-the-art solutions.The HFL model enhances low-light images with better visibility,less noise,and improved contrast,suitable for practical scenarios such as autonomous driving,medical imaging,and nighttime surveillance.Outperforming them by PSNR=27.26 dB,SSIM=0.93,and LPIPS=0.10 on benchmark dataset LOL-v1.The source code of HFL is available at https://github.com/Smile-QT/HFL-for-LIE.
基金supported by Key Research and Development Projects in Shaanxi Province (No. 2021GY-265)Xi’an University Talent Service Enterprise Project (No.2020KJRC0049)。
文摘In complex industrial scenes,it is difficult to acquire high-precision non-cooperative target pose under monocular visual servo control.This paper presents a new method of target extraction and high-precision edge fitting for the wheel of the sintering trolley in steel production,which fuses multiple target extraction algorithms adapting to the working environment of the target.Firstly,based on obvious difference between the pixels of the target image and the non-target image in the gray histogram,these pixels were classified and then segmented in intraclass,removing interference factors and remaining the target image.Then,multiple segmentation results were merged and a final target image was obtained after small connected regions were eliminated.In the edge fitting stage,the edge fitting method with best-circumscribed rectangle was proposed to accurately fit the circular target edge.Finally,PnP algorithm was adopted for pose measurement of the target.The experimental results showed that the average estimation error of pose angleγwith respect to the z-axis rotation was 0.2346°,the average measurement error of pose angleαwith respect to the x-axis rotation was 0.1703°,and the average measurement error of pose angle β with respect to the y-axis rotation was 0.2275°.The proposed method has practical application value.
基金supported by the National Natural Science Foundation of China(32171797)Chunhui Project Foundation of the Education Department of China(HZKY20220026).
文摘With rapid urbanization,fires pose significant challenges in urban governance.Traditional fire detection methods often struggle to detect smoke in complex urban scenes due to environmental interferences and variations in viewing angles.This study proposes a novel multimodal smoke detection method that fuses infrared and visible imagery using a transformer-based deep learning model.By capturing both thermal and visual cues,our approach significantly enhances the accuracy and robustness of smoke detection in business parks scenes.We first established a dual-view dataset comprising infrared and visible light videos,implemented an innovative image feature fusion strategy,and designed a deep learning model based on the transformer architecture and attention mechanism for smoke classification.Experimental results demonstrate that our method outperforms existing methods,under the condition of multi-view input,it achieves an accuracy rate of 90.88%,precision rate of 98.38%,recall rate of 92.41%and false positive and false negative rates both below 5%,underlining the effectiveness of the proposed multimodal and multi-view fusion approach.The attention mechanism plays a crucial role in improving detection performance,particularly in identifying subtle smoke features.
基金National Natural Science Foundation of China,Grant/Award Number:62173098,62104047Guangdong Provincial Key Laboratory of Cyber-Physical System,Grant/Award Number:2020B1212060069。
文摘Terahertz imaging technology has great potential applications in areas,such as remote sensing,navigation,security checks,and so on.However,terahertz images usually have the problems of heavy noises and low resolution.Previous terahertz image denoising methods are mainly based on traditional image processing methods,which have limited denoising effects on the terahertz noise.Existing deep learning-based image denoising methods are mostly used in natural images and easily cause a large amount of detail loss when denoising terahertz images.Here,a residual-learning-based multiscale hybridconvolution residual network(MHRNet)is proposed for terahertz image denoising,which can remove noises while preserving detail features in terahertz images.Specifically,a multiscale hybrid-convolution residual block(MHRB)is designed to extract rich detail features and local prediction residual noise from terahertz images.Specifically,MHRB is a residual structure composed of a multiscale dilated convolution block,a bottleneck layer,and a multiscale convolution block.MHRNet uses the MHRB and global residual learning to achieve terahertz image denoising.Ablation studies are performed to validate the effectiveness of MHRB.A series of experiments are conducted on the public terahertz image datasets.The experimental results demonstrate that MHRNet has an excellent denoising effect on synthetic and real noisy terahertz images.Compared with existing methods,MHRNet achieves comprehensive competitive results.
基金Supported by National Natural Science Foundation of China(Grant No.52175528)。
文摘In micro milling machining,tool wear directly affects workpiece quality and accuracy,making effective tool wear monitoring a key factor in ensuring product integrity.The use of machine vision-based methods can provide an intuitive and efficient representation of tool wear conditions.However,micro milling tools have non-flat flanks,thin coatings can peel off,and spindle orientation is uncertain during downtime.These factors result in low pixel values,uneven illumination,and arbitrary tool position.To address this,we propose an image-based tool wear monitoring method.It combines multiple algorithms to restore lost pixels due to uneven illumination during segmentation and accurately extract wear areas.Experimental results demonstrate that the proposed algorithm exhibits high robustness to such images,effectively addressing the effects of illumination and spindle orientation.Additionally,the algorithm has low complexity,fast execution time,and significantly reduces the detection time in situ.
基金supported by the National Key R&D Program of China (2022YFF0503400)the National Natural Science Foundation of China grant (U1931208)China Manned Space Program through its Space Application System.
文摘A detector's nondestructive readout mode allows its pixels to be read multiple times during integration,enabling generation of a series of"up-the-ramp"images that continuously accumulate photons between successive frames.Because noise is correlated across these images,optimal stacking generally requires the images to be weighted unequally to achieve the best possible target signal-to-noise ratio(SNR).Objects in the sky present wildly varied brightness characteristics,and the counts in individual pixels of the same object can also span wide ranges.Therefore,a single set of weights cannot be optimal in all cases.To ensure that the stacked image is easily calibratable,we apply the same weight to all pixels within the same frame.In practice,results for high-SNR cases degraded only slightly when we used weights derived for low-SNR cases,whereas the low-SNR cases remained more sensitive to the weights.Therefore,we propose a quasi-optimal stacking method that maximizes the stacked SNR for the case where the RSN=1 per pixel in the last frame and use simulated data to demonstrate that this approach enhances the SNR more strongly than the equal-weight stacking and ramp fitting methods.Furthermore,we estimate the improvements in the limiting magnitudes for the China Space Station Telescope using the proposed method.When compared with the conventional readout mode,which is equivalent to selecting the last frame from the nondestructive readout,stacking 30 up-the-ramp images can improve the limiting magnitude by approximately 0.5 mag for the telescope's near-infrared observations,effectively reducing readout noise by approximately 62%.
基金supported by the National Natural Science Foundation of China(Grant No.11802332)the China Scholarship Council(Grant No.202206435003)the Fundamental Research Funds for the Central Universities(Grant No.2024ZKPYLJ03).
文摘The internal microstructures of rock materials, including mineral heterogeneity and intrinsic microdefects, exert a significant influence on their nonlinear mechanical and cracking behaviors. It is of great significance to accurately characterize the actual microstructures and their influence on stress and damage evolution inside the rocks. In this study, an image-based fast Fourier transform (FFT) method is developed for reconstructing the actual rock microstructures by combining it with the digital image processing (DIP) technique. A series of experimental investigations were conducted to acquire information regarding the actual microstructure and the mechanical properties. Based on these experimental evidences, the processed microstructure information, in conjunction with the proposed micromechanical model, is incorporated into the numerical calculation. The proposed image-based FFT method was firstly validated through uniaxial compression tests. Subsequently, it was employed to predict and analyze the influence of microstructure on macroscopic mechanical behaviors, local stress distribution and the internal crack evolution process in brittle rocks. The distribution of feldspar is considerably more heterogeneous and scattered than that of quartz, which results in a greater propensity for the formation of cracks in feldspar. It is observed that initial cracks and new cracks, including intragranular and boundary ones, ultimately coalesce and connect as the primary through cracks, which are predominantly distributed along the boundary of the feldspar. This phenomenon is also predicted by the proposed numerical method. The results indicate that the proposed numerical method provides an effective approach for analyzing, understanding and predicting the nonlinear mechanical and cracking behaviors of brittle rocks by taking into account the actual microstructure characteristics.
