This study utilizes a visualization nozzle and spray experimental platform to experimentally investigate the flow focusing/blurring nozzle.It is found that the working mode of the nozzle transitions from flow focusing...This study utilizes a visualization nozzle and spray experimental platform to experimentally investigate the flow focusing/blurring nozzle.It is found that the working mode of the nozzle transitions from flow focusing to flow transition and eventually to flow blurring as the gas flow rate increases or the tube hole distance decreases.Conversely,an increase in liquid flow rate only facilitates the transition from flow focusing to flow transition.Changes in the gas/liquid flow rate or tube hole distance influence the gas shear effect and the gas inertial impact effect inside the nozzle,which in turn alters the working mode.An increase in gas flow rate results in a shift of the droplet size distribution towards smaller particle sizes in the flow blurring mode,whereas an increase in liquid flow rate produces the opposite effect.Notably,the impact of the gas flow rate on these changes is more pronounced than that of the liquid flow rate.展开更多
The rotary motion deblurring is an inevitable procedure when the imaging seeker is mounted in the rotating missiles.Traditional rotary motion deblurring methods suffer from ringing artifacts and noise,especially for l...The rotary motion deblurring is an inevitable procedure when the imaging seeker is mounted in the rotating missiles.Traditional rotary motion deblurring methods suffer from ringing artifacts and noise,especially for large blur extents.To solve the above problems,we propose a progressive rotary motion deblurring framework consisting of a coarse deblurring stage and a refinement stage.In the first stage,we design an adaptive blur extents factor(BE factor)to balance noise suppression and details reconstruction.And a novel deconvolution model is proposed based on BE factor.In the second stage,a triplescale deformable module CNN(TDM-CNN)is designed to reduce the ringing artifacts,which can exploit the 2D information of an image and adaptively adjust spatial sampling locations.To establish a standard evaluation benchmark,a real-world rotary motion blur dataset is proposed and released,which includes rotary blurred images and corresponding ground truth images with different blur angles.Experimental results demonstrate that the proposed method outperforms the state-of-the-art models on synthetic and real-world rotary motion blur datasets.The code and dataset are available at https://github.com/JinhuiQin/RotaryDeblurring.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
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
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
Reducing the defocus blur that arises from the finite aperture size and short exposure time is an essential problem in computational photography.It is very challenging because the blur kernel is spatially varying and ...Reducing the defocus blur that arises from the finite aperture size and short exposure time is an essential problem in computational photography.It is very challenging because the blur kernel is spatially varying and difficult to estimate by traditional methods.Due to its great breakthrough in low-level tasks,convolutional neural networks(CNNs)have been introdu-ced to the defocus deblurring problem and achieved significant progress.However,previous methods apply the same learned kernel for different regions of the defocus blurred images,thus it is difficult to handle nonuniform blurred images.To this end,this study designs a novel blur-aware multi-branch network(Ba-MBNet),in which different regions are treated differentially.In particular,we estimate the blur amounts of different regions by the internal geometric constraint of the dual-pixel(DP)data,which measures the defocus disparity between the left and right views.Based on the assumption that different image regions with different blur amounts have different deblurring difficulties,we leverage different networks with different capacities to treat different image regions.Moreover,we introduce a meta-learning defocus mask generation algorithm to assign each pixel to a proper branch.In this way,we can expect to maintain the information of the clear regions well while recovering the missing details of the blurred regions.Both quantitative and qualitative experiments demonstrate that our BaMBNet outperforms the state-of-the-art(SOTA)methods.For the dual-pixel defocus deblurring(DPD)-blur dataset,the proposed BaMBNet achieves 1.20 dB gain over the previous SOTA method in term of peak signal-to-noise ratio(PSNR)and reduces learnable parameters by 85%.The details of the code and dataset are available at https://github.com/junjun-jiang/BaMBNet.展开更多
Detection and classification of the blurred and the non-blurred regions in images is a challenging task due to the limited available information about blur type,scenarios and level of blurriness.In this paper,we propo...Detection and classification of the blurred and the non-blurred regions in images is a challenging task due to the limited available information about blur type,scenarios and level of blurriness.In this paper,we propose an effective method for blur detection and segmentation based on transfer learning concept.The proposed method consists of two separate steps.In the first step,genetic programming(GP)model is developed that quantify the amount of blur for each pixel in the image.The GP model method uses the multiresolution features of the image and it provides an improved blur map.In the second phase,the blur map is segmented into blurred and non-blurred regions by using an adaptive threshold.A model based on support vector machine(SVM)is developed to compute adaptive threshold for the input blur map.The performance of the proposed method is evaluated using two different datasets and compared with various state-of-the-art methods.The comparative analysis reveals that the proposed method performs better against the state-of-the-art techniques.展开更多
Enormousmethods have been proposed for the detection and segmentation of blur and non-blur regions of the images.Due to the limited available information about blur type,scenario and the level of blurriness,detection ...Enormousmethods have been proposed for the detection and segmentation of blur and non-blur regions of the images.Due to the limited available information about blur type,scenario and the level of blurriness,detection and segmentation is a challenging task.Hence,the performance of the blur measure operator is an essential factor and needs improvement to attain perfection.In this paper,we propose an effective blur measure based on local binary pattern(LBP)with adaptive threshold for blur detection.The sharpness metric developed based on LBP used a fixed threshold irrespective of the type and level of blur,that may not be suitable for images with variations in imaging conditions,blur amount and type.Contrarily,the proposed measure uses an adaptive threshold for each input image based on the image and blur properties to generate improved sharpness metric.The adaptive threshold is computed based on the model learned through support vector machine(SVM).The performance of the proposed method is evaluated using two different datasets and is compared with five state-of-the-art methods.Comparative analysis reveals that the proposed method performs significantly better qualitatively and quantitatively against all of the compared methods.展开更多
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
Human visual sense has two aspects in our feeling for blurred image, that is, one is the amount of blur depending on object size, the other is the amount of blur independent of the object size. In the former for examp...Human visual sense has two aspects in our feeling for blurred image, that is, one is the amount of blur depending on object size, the other is the amount of blur independent of the object size. In the former for example, when the image size becomes larger, the author feels smaller amount blur. The quantitative evaluation based on entropy for blurred images is proposed in this paper. The author calls this metric ＂variation entropy＂. This metric has two kinds of aspects that coincide with the human visual sense. The first is the absolute evaluation of blur, and the second is the relative evaluation of blur. The former can be quantified by variation entropy for a unit boundary length （or L-type variation entropy： HL ）, which is dependent on resolution, and the latter can be quantified by variation entropy for a unit area （or A-type variation entropy： H^A ）, which is independent of resolution. These two metrics have complementary properties. At last, two variation entropies are applied to the standard kanji character database, and then the strong relation between variation entropy and accuracy of recognition is discussed. The tendency of writing skills for grades is evaluated by applying the metric to a database collected from school children.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
Online defect visual inspection (ODVI) works while the object has to be static, otherwise the relative motion between camera and object will create motion blur in images. In order to implement ODVI in dynamic scene, i...Online defect visual inspection (ODVI) works while the object has to be static, otherwise the relative motion between camera and object will create motion blur in images. In order to implement ODVI in dynamic scene, it developes one blind motion deblurring method whose objective is to estimate blur kernel parameters precisely. In the proposed method, Radon transform on superpixels determinated the blur angle, and the autocorrelation function based on magnitude (AFM) of the preprocessed blurred image was utilized to identify the blur length. With the projection relationship discussed in this study, it will be unnecessary to rotate the blurred image or the axis. The proposed method is of high accuracy and robustness to noise, and it can somehow handle saturated pixels. To validate the proposed method, experiments have been carried out on synthetic images both in noise free and noisy situations. The results show that the method outperforms existing approaches. With the modified Richardson– Lucy deconvolution, it demonstrates that the proposed method is effective for ODVI in terms of subjective visual quality.展开更多
The degraded parameters recognition is very important for the restoration of blurred images. There are two common types of blurs for most camera systems. One is the defocus blur due to the optical system's defocus...The degraded parameters recognition is very important for the restoration of blurred images. There are two common types of blurs for most camera systems. One is the defocus blur due to the optical system's defocus phenomenon and the other is the motion blur due to the relative movement between the objectives and the camera. Compared with the recognition for the blurred image with only one blur model, the parameter estimation for the picture combining defocus and motion blur models is a more complicated mission. A method was proposed for computer to estimate the parameters of defocus blur and motion blur in cepstrum area simultaneously. According to characters of both blur models in the frequency domain, an adjustment approach was suggested in the frequency area and then convert to the cepstrum field to increase the accuracy of measurement.展开更多
The 3D reconstruction using deep learning-based intelligent systems can provide great help for measuring an individual’s height and shape quickly and accurately through 2D motion-blurred images.Generally,during the a...The 3D reconstruction using deep learning-based intelligent systems can provide great help for measuring an individual’s height and shape quickly and accurately through 2D motion-blurred images.Generally,during the acquisition of images in real-time,motion blur,caused by camera shaking or human motion,appears.Deep learning-based intelligent control applied in vision can help us solve the problem.To this end,we propose a 3D reconstruction method for motion-blurred images using deep learning.First,we develop a BF-WGAN algorithm that combines the bilateral filtering(BF)denoising theory with a Wasserstein generative adversarial network(WGAN)to remove motion blur.The bilateral filter denoising algorithm is used to remove the noise and to retain the details of the blurred image.Then,the blurred image and the corresponding sharp image are input into the WGAN.This algorithm distinguishes the motion-blurred image from the corresponding sharp image according to the WGAN loss and perceptual loss functions.Next,we use the deblurred images generated by the BFWGAN algorithm for 3D reconstruction.We propose a threshold optimization random sample consensus(TO-RANSAC)algorithm that can remove the wrong relationship between two views in the 3D reconstructed model relatively accurately.Compared with the traditional RANSAC algorithm,the TO-RANSAC algorithm can adjust the threshold adaptively,which improves the accuracy of the 3D reconstruction results.The experimental results show that our BF-WGAN algorithm has a better deblurring effect and higher efficiency than do other representative algorithms.In addition,the TO-RANSAC algorithm yields a calculation accuracy considerably higher than that of the traditional RANSAC algorithm.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
In this paper image with horizontal motion blur, vertical motion blur and angled motion blur are considered. We construct several difference schemes to the highly nonlinear term △↓.（△↓u/√｜△↓｜^2＋β） of the ...In this paper image with horizontal motion blur, vertical motion blur and angled motion blur are considered. We construct several difference schemes to the highly nonlinear term △↓.（△↓u/√｜△↓｜^2＋β） of the total variation-based image motion deblurring problem. The large nonlinear system is linearized by fixed point iteration method. An algebraic multigrid method with Krylov subspace acceleration is used to solve the corresponding linear equations as in [7]. The algorithms can restore the image very well. We give some numerical experiments to demonstrate that our difference schemes are efficient and robust.展开更多
Successful restoration of blurred images depends primarily on the knowledge about the degradationparameter.Defocus blur model in the frequency domain is characterized by concentric rings and the blurradius of the poin...Successful restoration of blurred images depends primarily on the knowledge about the degradationparameter.Defocus blur model in the frequency domain is characterized by concentric rings and the blurradius of the point spread function(PSF)can be identified conveniently in the frequency field for peopleby manual means rather than for computer.This paper introduces a practical method for computer to esti-mate the defocus blur parameter in cepstrum area.Fourier transform plays an intermediate role in the pathto cepstrum domain.We suggest a weighted adjustment operation in the frequency domain and then con-vert it to the cepstrum field to increase the accuracy of recognition.展开更多
基金the National Natural Science Foundation of China(52276026).
文摘This study utilizes a visualization nozzle and spray experimental platform to experimentally investigate the flow focusing/blurring nozzle.It is found that the working mode of the nozzle transitions from flow focusing to flow transition and eventually to flow blurring as the gas flow rate increases or the tube hole distance decreases.Conversely,an increase in liquid flow rate only facilitates the transition from flow focusing to flow transition.Changes in the gas/liquid flow rate or tube hole distance influence the gas shear effect and the gas inertial impact effect inside the nozzle,which in turn alters the working mode.An increase in gas flow rate results in a shift of the droplet size distribution towards smaller particle sizes in the flow blurring mode,whereas an increase in liquid flow rate produces the opposite effect.Notably,the impact of the gas flow rate on these changes is more pronounced than that of the liquid flow rate.
基金the National Natural Science Foundation of China under Grant 62075169,Grant 62003247,and Grant 62061160370the Hubei Province Key Research and Development Program under Grant 2021BBA235the Zhuhai Basic and Applied Basic Research Foundation under Grant ZH22017003200010PWC.
文摘The rotary motion deblurring is an inevitable procedure when the imaging seeker is mounted in the rotating missiles.Traditional rotary motion deblurring methods suffer from ringing artifacts and noise,especially for large blur extents.To solve the above problems,we propose a progressive rotary motion deblurring framework consisting of a coarse deblurring stage and a refinement stage.In the first stage,we design an adaptive blur extents factor(BE factor)to balance noise suppression and details reconstruction.And a novel deconvolution model is proposed based on BE factor.In the second stage,a triplescale deformable module CNN(TDM-CNN)is designed to reduce the ringing artifacts,which can exploit the 2D information of an image and adaptively adjust spatial sampling locations.To establish a standard evaluation benchmark,a real-world rotary motion blur dataset is proposed and released,which includes rotary blurred images and corresponding ground truth images with different blur angles.Experimental results demonstrate that the proposed method outperforms the state-of-the-art models on synthetic and real-world rotary motion blur datasets.The code and dataset are available at https://github.com/JinhuiQin/RotaryDeblurring.
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.
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supported by the National Natural Science Foundation of China (61971165, 61922027, 61773295)in part by the Fundamental Research Funds for the Central Universities (FRFCU5710050119)+1 种基金the Natural Science Foundation of Heilongjiang Province(YQ2020F004)the Chinese Association for Artificial Intelligence(CAAI)-Huawei Mind Spore Open Fund
文摘Reducing the defocus blur that arises from the finite aperture size and short exposure time is an essential problem in computational photography.It is very challenging because the blur kernel is spatially varying and difficult to estimate by traditional methods.Due to its great breakthrough in low-level tasks,convolutional neural networks(CNNs)have been introdu-ced to the defocus deblurring problem and achieved significant progress.However,previous methods apply the same learned kernel for different regions of the defocus blurred images,thus it is difficult to handle nonuniform blurred images.To this end,this study designs a novel blur-aware multi-branch network(Ba-MBNet),in which different regions are treated differentially.In particular,we estimate the blur amounts of different regions by the internal geometric constraint of the dual-pixel(DP)data,which measures the defocus disparity between the left and right views.Based on the assumption that different image regions with different blur amounts have different deblurring difficulties,we leverage different networks with different capacities to treat different image regions.Moreover,we introduce a meta-learning defocus mask generation algorithm to assign each pixel to a proper branch.In this way,we can expect to maintain the information of the clear regions well while recovering the missing details of the blurred regions.Both quantitative and qualitative experiments demonstrate that our BaMBNet outperforms the state-of-the-art(SOTA)methods.For the dual-pixel defocus deblurring(DPD)-blur dataset,the proposed BaMBNet achieves 1.20 dB gain over the previous SOTA method in term of peak signal-to-noise ratio(PSNR)and reduces learnable parameters by 85%.The details of the code and dataset are available at https://github.com/junjun-jiang/BaMBNet.
基金This work was supported by the BK-21 FOUR program through National Research Foundation of Korea(NRF)under Ministry of Education.
文摘Detection and classification of the blurred and the non-blurred regions in images is a challenging task due to the limited available information about blur type,scenarios and level of blurriness.In this paper,we propose an effective method for blur detection and segmentation based on transfer learning concept.The proposed method consists of two separate steps.In the first step,genetic programming(GP)model is developed that quantify the amount of blur for each pixel in the image.The GP model method uses the multiresolution features of the image and it provides an improved blur map.In the second phase,the blur map is segmented into blurred and non-blurred regions by using an adaptive threshold.A model based on support vector machine(SVM)is developed to compute adaptive threshold for the input blur map.The performance of the proposed method is evaluated using two different datasets and compared with various state-of-the-art methods.The comparative analysis reveals that the proposed method performs better against the state-of-the-art techniques.
基金This work is supported by the BK-21 FOUR program and by the Creative Challenge Research Program(2021R1I1A1A01052521)through National Research Foundation of Korea(NRF)under Ministry of Education,Korea.
文摘Enormousmethods have been proposed for the detection and segmentation of blur and non-blur regions of the images.Due to the limited available information about blur type,scenario and the level of blurriness,detection and segmentation is a challenging task.Hence,the performance of the blur measure operator is an essential factor and needs improvement to attain perfection.In this paper,we propose an effective blur measure based on local binary pattern(LBP)with adaptive threshold for blur detection.The sharpness metric developed based on LBP used a fixed threshold irrespective of the type and level of blur,that may not be suitable for images with variations in imaging conditions,blur amount and type.Contrarily,the proposed measure uses an adaptive threshold for each input image based on the image and blur properties to generate improved sharpness metric.The adaptive threshold is computed based on the model learned through support vector machine(SVM).The performance of the proposed method is evaluated using two different datasets and is compared with five state-of-the-art methods.Comparative analysis reveals that the proposed method performs significantly better qualitatively and quantitatively against all of the compared methods.
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Human visual sense has two aspects in our feeling for blurred image, that is, one is the amount of blur depending on object size, the other is the amount of blur independent of the object size. In the former for example, when the image size becomes larger, the author feels smaller amount blur. The quantitative evaluation based on entropy for blurred images is proposed in this paper. The author calls this metric ＂variation entropy＂. This metric has two kinds of aspects that coincide with the human visual sense. The first is the absolute evaluation of blur, and the second is the relative evaluation of blur. The former can be quantified by variation entropy for a unit boundary length （or L-type variation entropy： HL ）, which is dependent on resolution, and the latter can be quantified by variation entropy for a unit area （or A-type variation entropy： H^A ）, which is independent of resolution. These two metrics have complementary properties. At last, two variation entropies are applied to the standard kanji character database, and then the strong relation between variation entropy and accuracy of recognition is discussed. The tendency of writing skills for grades is evaluated by applying the metric to a database collected from school children.
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Online defect visual inspection (ODVI) works while the object has to be static, otherwise the relative motion between camera and object will create motion blur in images. In order to implement ODVI in dynamic scene, it developes one blind motion deblurring method whose objective is to estimate blur kernel parameters precisely. In the proposed method, Radon transform on superpixels determinated the blur angle, and the autocorrelation function based on magnitude (AFM) of the preprocessed blurred image was utilized to identify the blur length. With the projection relationship discussed in this study, it will be unnecessary to rotate the blurred image or the axis. The proposed method is of high accuracy and robustness to noise, and it can somehow handle saturated pixels. To validate the proposed method, experiments have been carried out on synthetic images both in noise free and noisy situations. The results show that the method outperforms existing approaches. With the modified Richardson– Lucy deconvolution, it demonstrates that the proposed method is effective for ODVI in terms of subjective visual quality.
基金The National Natural Science Foundation of China (No 30570485)
文摘The degraded parameters recognition is very important for the restoration of blurred images. There are two common types of blurs for most camera systems. One is the defocus blur due to the optical system's defocus phenomenon and the other is the motion blur due to the relative movement between the objectives and the camera. Compared with the recognition for the blurred image with only one blur model, the parameter estimation for the picture combining defocus and motion blur models is a more complicated mission. A method was proposed for computer to estimate the parameters of defocus blur and motion blur in cepstrum area simultaneously. According to characters of both blur models in the frequency domain, an adjustment approach was suggested in the frequency area and then convert to the cepstrum field to increase the accuracy of measurement.
基金the National Natural Science Foundation of China under Grant 61902311in part by the Japan Society for the Promotion of Science(JSPS)Grants-in-Aid for Scientific Research(KAKENHI)under Grant JP18K18044.
文摘The 3D reconstruction using deep learning-based intelligent systems can provide great help for measuring an individual’s height and shape quickly and accurately through 2D motion-blurred images.Generally,during the acquisition of images in real-time,motion blur,caused by camera shaking or human motion,appears.Deep learning-based intelligent control applied in vision can help us solve the problem.To this end,we propose a 3D reconstruction method for motion-blurred images using deep learning.First,we develop a BF-WGAN algorithm that combines the bilateral filtering(BF)denoising theory with a Wasserstein generative adversarial network(WGAN)to remove motion blur.The bilateral filter denoising algorithm is used to remove the noise and to retain the details of the blurred image.Then,the blurred image and the corresponding sharp image are input into the WGAN.This algorithm distinguishes the motion-blurred image from the corresponding sharp image according to the WGAN loss and perceptual loss functions.Next,we use the deblurred images generated by the BFWGAN algorithm for 3D reconstruction.We propose a threshold optimization random sample consensus(TO-RANSAC)algorithm that can remove the wrong relationship between two views in the 3D reconstructed model relatively accurately.Compared with the traditional RANSAC algorithm,the TO-RANSAC algorithm can adjust the threshold adaptively,which improves the accuracy of the 3D reconstruction results.The experimental results show that our BF-WGAN algorithm has a better deblurring effect and higher efficiency than do other representative algorithms.In addition,the TO-RANSAC algorithm yields a calculation accuracy considerably higher than that of the traditional RANSAC algorithm.
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In this paper image with horizontal motion blur, vertical motion blur and angled motion blur are considered. We construct several difference schemes to the highly nonlinear term △↓.（△↓u/√｜△↓｜^2＋β） of the total variation-based image motion deblurring problem. The large nonlinear system is linearized by fixed point iteration method. An algebraic multigrid method with Krylov subspace acceleration is used to solve the corresponding linear equations as in [7]. The algorithms can restore the image very well. We give some numerical experiments to demonstrate that our difference schemes are efficient and robust.
基金the National Natural Science Foundation of China(No.30570485)
文摘Successful restoration of blurred images depends primarily on the knowledge about the degradationparameter.Defocus blur model in the frequency domain is characterized by concentric rings and the blurradius of the point spread function(PSF)can be identified conveniently in the frequency field for peopleby manual means rather than for computer.This paper introduces a practical method for computer to esti-mate the defocus blur parameter in cepstrum area.Fourier transform plays an intermediate role in the pathto cepstrum domain.We suggest a weighted adjustment operation in the frequency domain and then con-vert it to the cepstrum field to increase the accuracy of recognition.
