Background For static scenes with multiple depth layers,existing defocused image deblurring methods have the problems of edge-ringing artifacts or insufficient deblurring owing to inaccurate estimation of the blur amo...Background For static scenes with multiple depth layers,existing defocused image deblurring methods have the problems of edge-ringing artifacts or insufficient deblurring owing to inaccurate estimation of the blur amount,and prior knowledge in nonblind deconvolution is not strong,which leads to image detail recovery challenges.Methods To this end,this study proposes a blur map estimation method for defocused images based on the gradient difference of the boundary neighborhood,which uses the gradient difference of the boundary neighborhood to accurately obtain the amount of blurring,thereby preventing boundary ringing artifacts.The obtained blur map is then used for blur detection to determine whether the image needs to be deblurred,thereby improving the efficiency of deblurring without manual intervention and judgment.Finally,a nonblind deconvolution algorithm was designed to achieve image deblurring based on the blur amount selection strategy and sparse prior.Results Experimental results showed that our method improves PSNR(Peak Signal-to-Noise Ratio)and SSIM(Structural Similarity Index)by an average of 4.6%and 7.3%,respectively,compared to existing methods.Conclusions Experimental results showed that the proposed method outperforms existing methods.Compared to existing methods,our method can better solve the problems of boundary ringing artifacts and detail information preservation in defocused image deblurring.展开更多
In imaging on moving target, it is easy to get space- variant blurred image. In order to recover the image and gain recognizable target, an approach to recover the space-variant blurred image is presented based on ima...In imaging on moving target, it is easy to get space- variant blurred image. In order to recover the image and gain recognizable target, an approach to recover the space-variant blurred image is presented based on image segmentation. Be- cause of motion blur＇s convolution process, the pixels of observed image＇s target and background will be displaced and piled up to produce two superposition regions. As a result, the neighbor- ing pixels in the superposition regions will have similar grey level change. According to the pixel＇s motion-blur character, the target＇s blurred edge of superposition region could be detected. Canny operator can be recurred to detect the target edge which parallels the motion blur direction. Then in the segmentation process, the whole target image which has the character of integral convolution between motion blur and real target image can be obtained. At last, the target image is restored by deconvolution algorithms with adding zeros. The restoration result indicates that the approach can effectively solve the kind of problem of space-variant motion blurred image restoration.展开更多
A novel single-channel blind separation algorithm for permuted motion blurred images is proposed by using blind restoration in this paper. Both the motion direction and the length of the point spread function （PSF） ...A novel single-channel blind separation algorithm for permuted motion blurred images is proposed by using blind restoration in this paper. Both the motion direction and the length of the point spread function （PSF） are estimated by Radon transformation and extrema a detection. Using the estimated blur parameters, the permuted image is restored by performing the L-R blind restoration method. The permutation mixing matrices can be accurately estimated by classifying the ringing effect in the restored image, thereby the source images can be separated. Simulation results show a better separation efficiency for the permuted motion blurred image with various permutation operations. The proposed algorithm indicates a better performance on the robustness against Gaussian noise and lossy JPEG compression.展开更多
be stored or transmitted in an efficient form.In this work,a new idea is proposed,where we take advantage of the redundancy that appears in a group of images to be all compressed together,instead of compressing each i...be stored or transmitted in an efficient form.In this work,a new idea is proposed,where we take advantage of the redundancy that appears in a group of images to be all compressed together,instead of compressing each image by itself.In our proposed technique,a classification process is applied,where the set of the input images are classified into groups based on existing technique like L1 and L2 norms,color histograms.All images that belong to the same group are compressed based on dividing the images of the same group into sub-images of equal sizes and saving the references into a codebook.In the process of extracting the different sub-images,we used the mean squared error for comparison and three blurring methods(simple,middle and majority blurring)to increase the compression ratio.Experiments show that varying blurring values,as well as MSE thresholds,enhanced the compression results in a group of images compared to JPEG and PNG compressors.展开更多
Background:Understanding the neurophysiological mechanisms of Amblyopia,a neurodevelopmental disorder of the visual cortex,will bring us closer to full recovery.Past findings have been contradictory.Results have shown...Background:Understanding the neurophysiological mechanisms of Amblyopia,a neurodevelopmental disorder of the visual cortex,will bring us closer to full recovery.Past findings have been contradictory.Results have shown that despite having severe acuity impairment,amblyopes can nonetheless perceive sharp edges.In this study,we explore the representation of blur through a series of image blur-discrimination and matching tasks,to understand more about the amblyopes’visual system.Methods:Monocular image blur-discrimination thresholds were measured in a spatial two-alternative forced-choice procedure whereby subjects had to decide which image was the blurriest.Subjects also had to interocularly match pictures that were identical to those used for the image blur discrimination task.Ten amblyopes,as well as a group of ten controls were under study.Results:Data on amblyopes and controls will be presented for both experiments.According to previous research that was done on blur-edge discrimination and matching,we predict that subjects’performance will follow a dipper function,that is,all observers will be better at discriminating between both images when a small amount of blur is applied rather than when the image is either sharp or very blurry.We also predict that amblyopes’blur discrimination will be noisier,but that they will paradoxically be able to match the sharpness of the images presented in the matching task.Conclusions:This would confirm our hypothesis about amblyopes’visual system,that they can represent blur levels defined by spatial frequencies that are beyond their resolution limit,and would also raise interesting questions about the visual system in general regarding the different perceptions driven by images versus edges.展开更多
BACKGROUND Digital pathology image(DPI)analysis has been developed by machine learning(ML)techniques.However,little attention has been paid to the reproducibility of ML-based histological classification in heterochron...BACKGROUND Digital pathology image(DPI)analysis has been developed by machine learning(ML)techniques.However,little attention has been paid to the reproducibility of ML-based histological classification in heterochronously obtained DPIs of the same hematoxylin and eosin(HE)slide.AIM To elucidate the frequency and preventable causes of discordant classification results of DPI analysis using ML for the heterochronously obtained DPIs.METHODS We created paired DPIs by scanning 298 HE stained slides containing 584 tissues twice with a virtual slide scanner.The paired DPIs were analyzed by our MLaided classification model.We defined non-flipped and flipped groups as the paired DPIs with concordant and discordant classification results,respectively.We compared differences in color and blur between the non-flipped and flipped groups by L1-norm and a blur index,respectively.RESULTS We observed discordant classification results in 23.1%of the paired DPIs obtained by two independent scans of the same microscope slide.We detected no significant difference in the L1-norm of each color channel between the two groups;however,the flipped group showed a significantly higher blur index than the non-flipped group.CONCLUSION Our results suggest that differences in the blur-not the color-of the paired DPIs may cause discordant classification results.An ML-aided classification model for DPI should be tested for this potential cause of the reduced reproducibility of the model.In a future study,a slide scanner and/or a preprocessing method of minimizing DPI blur should be developed.展开更多
This paper introduces a new effective method to restore the uniform linear motion blurred im-age. The effect of the out-of-frame pixels on the blurring process and the estimate of these pixelsare analysed. The restora...This paper introduces a new effective method to restore the uniform linear motion blurred im-age. The effect of the out-of-frame pixels on the blurring process and the estimate of these pixelsare analysed. The restoration qualities of different deblurring methods are compared. Finally, theauthors come to a conclusion that it is impossible to determine the length of blurring movement infrequency domain.展开更多
Single image super-resolution(SISR)is a fundamentally challenging problem because a low-resolution(LR)image can correspond to a set of high-resolution(HR)images,while most are not expected.Recently,SISR can be achieve...Single image super-resolution(SISR)is a fundamentally challenging problem because a low-resolution(LR)image can correspond to a set of high-resolution(HR)images,while most are not expected.Recently,SISR can be achieved by a deep learning-based method.By constructing a very deep super-resolution convolutional neural network(VDSRCNN),the LR images can be improved to HR images.This study mainly achieves two objectives:image super-resolution(ISR)and deblurring the image from VDSRCNN.Firstly,by analyzing ISR,we modify different training parameters to test the performance of VDSRCNN.Secondly,we add the motion blurred images to the training set to optimize the performance of VDSRCNN.Finally,we use image quality indexes to evaluate the difference between the images from classical methods and VDSRCNN.The results indicate that the VDSRCNN performs better in generating HR images from LR images using the optimized VDSRCNN in a proper method.展开更多
Blur is produced in a digital image due to low passfiltering,moving objects or defocus of the camera lens during capture.Image viewers are annoyed by blur artefact and the image's perceived quality suffers as a re...Blur is produced in a digital image due to low passfiltering,moving objects or defocus of the camera lens during capture.Image viewers are annoyed by blur artefact and the image's perceived quality suffers as a result.The high-quality input is relevant to communication service providers and imaging product makers because it may help them improve their processes.Human-based blur assessment is time-consuming,expensive and must adhere to subjective evaluation standards.This paper presents a revolutionary no-reference blur assessment algorithm based on reblurring blurred images using a special mask developed with a Markov basis and Laplacefilter.Thefinal blur score of blurred images has been calculated from the local variation in horizontal and vertical pixel intensity of blurred and re-blurred images.The objective scores are generated by applying proposed algorithm on the two image databases i.e.,Laboratory for image and video engineering(LIVE)database and Tampere image database(TID 2013).Finally,on the basis of objective and subjective scores performance analysis is done in terms of Pearson linear correlation coefficient(PLCC),Spearman rank-order correlation coefficient(SROCC),Mean absolute error(MAE),Root mean square error(RMSE)and Outliers ratio(OR).The existing no-reference blur assessment algorithms have been used various methods for the evaluation of blur from no-reference image such as Just noticeable blur(JNB),Cumulative Probability Distribution of Blur Detection(CPBD)and Edge Model based Blur Metric(EMBM).The results illustrate that the proposed method was successful in predicting high blur scores with high accuracy as compared to existing no-reference blur assessment algorithms such as JNB,CPBD and EMBM algorithms.展开更多
基金Supported by the National Natural Science Foundation of China (62172190)the“Double Creation”Plan of Jiangsu Province (JSSCRC2021532)the“Taihu Talent-Innovative Leading Talent”Plan of Wuxi City (Certificate Date:202110)。
文摘Background For static scenes with multiple depth layers,existing defocused image deblurring methods have the problems of edge-ringing artifacts or insufficient deblurring owing to inaccurate estimation of the blur amount,and prior knowledge in nonblind deconvolution is not strong,which leads to image detail recovery challenges.Methods To this end,this study proposes a blur map estimation method for defocused images based on the gradient difference of the boundary neighborhood,which uses the gradient difference of the boundary neighborhood to accurately obtain the amount of blurring,thereby preventing boundary ringing artifacts.The obtained blur map is then used for blur detection to determine whether the image needs to be deblurred,thereby improving the efficiency of deblurring without manual intervention and judgment.Finally,a nonblind deconvolution algorithm was designed to achieve image deblurring based on the blur amount selection strategy and sparse prior.Results Experimental results showed that our method improves PSNR(Peak Signal-to-Noise Ratio)and SSIM(Structural Similarity Index)by an average of 4.6%and 7.3%,respectively,compared to existing methods.Conclusions Experimental results showed that the proposed method outperforms existing methods.Compared to existing methods,our method can better solve the problems of boundary ringing artifacts and detail information preservation in defocused image deblurring.
文摘In imaging on moving target, it is easy to get space- variant blurred image. In order to recover the image and gain recognizable target, an approach to recover the space-variant blurred image is presented based on image segmentation. Be- cause of motion blur＇s convolution process, the pixels of observed image＇s target and background will be displaced and piled up to produce two superposition regions. As a result, the neighbor- ing pixels in the superposition regions will have similar grey level change. According to the pixel＇s motion-blur character, the target＇s blurred edge of superposition region could be detected. Canny operator can be recurred to detect the target edge which parallels the motion blur direction. Then in the segmentation process, the whole target image which has the character of integral convolution between motion blur and real target image can be obtained. At last, the target image is restored by deconvolution algorithms with adding zeros. The restoration result indicates that the approach can effectively solve the kind of problem of space-variant motion blurred image restoration.
基金Project supported by the National Natural Science Foundation of China (Grant No.60872114)the Shanghai Leading Academic Discipline Project (Grant No.S30108)the Graduate Student Innovation Foundation of Shanghai University (Grant No.SHUCX101086)
文摘A novel single-channel blind separation algorithm for permuted motion blurred images is proposed by using blind restoration in this paper. Both the motion direction and the length of the point spread function （PSF） are estimated by Radon transformation and extrema a detection. Using the estimated blur parameters, the permuted image is restored by performing the L-R blind restoration method. The permutation mixing matrices can be accurately estimated by classifying the ringing effect in the restored image, thereby the source images can be separated. Simulation results show a better separation efficiency for the permuted motion blurred image with various permutation operations. The proposed algorithm indicates a better performance on the robustness against Gaussian noise and lossy JPEG compression.
文摘be stored or transmitted in an efficient form.In this work,a new idea is proposed,where we take advantage of the redundancy that appears in a group of images to be all compressed together,instead of compressing each image by itself.In our proposed technique,a classification process is applied,where the set of the input images are classified into groups based on existing technique like L1 and L2 norms,color histograms.All images that belong to the same group are compressed based on dividing the images of the same group into sub-images of equal sizes and saving the references into a codebook.In the process of extracting the different sub-images,we used the mean squared error for comparison and three blurring methods(simple,middle and majority blurring)to increase the compression ratio.Experiments show that varying blurring values,as well as MSE thresholds,enhanced the compression results in a group of images compared to JPEG and PNG compressors.
文摘Background:Understanding the neurophysiological mechanisms of Amblyopia,a neurodevelopmental disorder of the visual cortex,will bring us closer to full recovery.Past findings have been contradictory.Results have shown that despite having severe acuity impairment,amblyopes can nonetheless perceive sharp edges.In this study,we explore the representation of blur through a series of image blur-discrimination and matching tasks,to understand more about the amblyopes’visual system.Methods:Monocular image blur-discrimination thresholds were measured in a spatial two-alternative forced-choice procedure whereby subjects had to decide which image was the blurriest.Subjects also had to interocularly match pictures that were identical to those used for the image blur discrimination task.Ten amblyopes,as well as a group of ten controls were under study.Results:Data on amblyopes and controls will be presented for both experiments.According to previous research that was done on blur-edge discrimination and matching,we predict that subjects’performance will follow a dipper function,that is,all observers will be better at discriminating between both images when a small amount of blur is applied rather than when the image is either sharp or very blurry.We also predict that amblyopes’blur discrimination will be noisier,but that they will paradoxically be able to match the sharpness of the images presented in the matching task.Conclusions:This would confirm our hypothesis about amblyopes’visual system,that they can represent blur levels defined by spatial frequencies that are beyond their resolution limit,and would also raise interesting questions about the visual system in general regarding the different perceptions driven by images versus edges.
文摘BACKGROUND Digital pathology image(DPI)analysis has been developed by machine learning(ML)techniques.However,little attention has been paid to the reproducibility of ML-based histological classification in heterochronously obtained DPIs of the same hematoxylin and eosin(HE)slide.AIM To elucidate the frequency and preventable causes of discordant classification results of DPI analysis using ML for the heterochronously obtained DPIs.METHODS We created paired DPIs by scanning 298 HE stained slides containing 584 tissues twice with a virtual slide scanner.The paired DPIs were analyzed by our MLaided classification model.We defined non-flipped and flipped groups as the paired DPIs with concordant and discordant classification results,respectively.We compared differences in color and blur between the non-flipped and flipped groups by L1-norm and a blur index,respectively.RESULTS We observed discordant classification results in 23.1%of the paired DPIs obtained by two independent scans of the same microscope slide.We detected no significant difference in the L1-norm of each color channel between the two groups;however,the flipped group showed a significantly higher blur index than the non-flipped group.CONCLUSION Our results suggest that differences in the blur-not the color-of the paired DPIs may cause discordant classification results.An ML-aided classification model for DPI should be tested for this potential cause of the reduced reproducibility of the model.In a future study,a slide scanner and/or a preprocessing method of minimizing DPI blur should be developed.
文摘This paper introduces a new effective method to restore the uniform linear motion blurred im-age. The effect of the out-of-frame pixels on the blurring process and the estimate of these pixelsare analysed. The restoration qualities of different deblurring methods are compared. Finally, theauthors come to a conclusion that it is impossible to determine the length of blurring movement infrequency domain.
文摘Single image super-resolution(SISR)is a fundamentally challenging problem because a low-resolution(LR)image can correspond to a set of high-resolution(HR)images,while most are not expected.Recently,SISR can be achieved by a deep learning-based method.By constructing a very deep super-resolution convolutional neural network(VDSRCNN),the LR images can be improved to HR images.This study mainly achieves two objectives:image super-resolution(ISR)and deblurring the image from VDSRCNN.Firstly,by analyzing ISR,we modify different training parameters to test the performance of VDSRCNN.Secondly,we add the motion blurred images to the training set to optimize the performance of VDSRCNN.Finally,we use image quality indexes to evaluate the difference between the images from classical methods and VDSRCNN.The results indicate that the VDSRCNN performs better in generating HR images from LR images using the optimized VDSRCNN in a proper method.
文摘Blur is produced in a digital image due to low passfiltering,moving objects or defocus of the camera lens during capture.Image viewers are annoyed by blur artefact and the image's perceived quality suffers as a result.The high-quality input is relevant to communication service providers and imaging product makers because it may help them improve their processes.Human-based blur assessment is time-consuming,expensive and must adhere to subjective evaluation standards.This paper presents a revolutionary no-reference blur assessment algorithm based on reblurring blurred images using a special mask developed with a Markov basis and Laplacefilter.Thefinal blur score of blurred images has been calculated from the local variation in horizontal and vertical pixel intensity of blurred and re-blurred images.The objective scores are generated by applying proposed algorithm on the two image databases i.e.,Laboratory for image and video engineering(LIVE)database and Tampere image database(TID 2013).Finally,on the basis of objective and subjective scores performance analysis is done in terms of Pearson linear correlation coefficient(PLCC),Spearman rank-order correlation coefficient(SROCC),Mean absolute error(MAE),Root mean square error(RMSE)and Outliers ratio(OR).The existing no-reference blur assessment algorithms have been used various methods for the evaluation of blur from no-reference image such as Just noticeable blur(JNB),Cumulative Probability Distribution of Blur Detection(CPBD)and Edge Model based Blur Metric(EMBM).The results illustrate that the proposed method was successful in predicting high blur scores with high accuracy as compared to existing no-reference blur assessment algorithms such as JNB,CPBD and EMBM algorithms.
