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
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
Following publication of the original article[1],the statement of Data availability and Competing interests have been added.Data availability The datasets used and analyzed during this study are available from the cor...Following publication of the original article[1],the statement of Data availability and Competing interests have been added.Data availability The datasets used and analyzed during this study are available from the corresponding author upon reasonable request.展开更多
As for machine vision-based intelligent system in the application of discriminating and sorting the sex of silkworm pupae,the tail gonad was the unique physiological feature.However,motion blur,resulting from the live...As for machine vision-based intelligent system in the application of discriminating and sorting the sex of silkworm pupae,the tail gonad was the unique physiological feature.However,motion blur,resulting from the live silkworm pupa’s writhing motion at the moment of capturing image,could lose textures and structures(such as edge and tail gonad etc.)dramatically,which casted great challenges for sex identification.To increase the image quality and relieve the difficulty of discrimination caused by motion blur,an effective approach that including three stages was proposed in this work.In the image prediction stage,first sharp edges were acquired by using filtering techniques.Then the initial blur kernel was computed with Gaussian prior.The coarse version latent image was deconvoluted in the Fourier domain.In the kernel refinement stage,the Radon transform was applied to estimate the accurate kernel.In the final restoration step,a TV-L1 deconvolution model was carried out to obtain a better result.The experimental results showed that benefiting from the prediction step and kernel refinement step,the kernel was more accurate and the recovered image contained much more textures.It revealed that the proposed method was useful in removing the motion blur.Furthermore,the method could also be applied to other fields.展开更多
Accurate velocity measurement of unmanned aerial vehicles(UAVs)is essential in various applications.Traditional vision-based methods rely heavily on visual features,which are often inadequate in low-light or feature-s...Accurate velocity measurement of unmanned aerial vehicles(UAVs)is essential in various applications.Traditional vision-based methods rely heavily on visual features,which are often inadequate in low-light or feature-sparse environments.This study presents a novel approach to measure the axial velocity of UAVs using motion blur images captured by a UAV-mounted monocular camera.We introduce a motion blur model that synthesizes imaging from neighboring frames to enhance motion blur visibility.The synthesized blur frames are transformed into spectrograms using the Fast Fourier Transform(FFT)technique.We then apply a binarization process and the Radon transform to extract light-dark stripe spacing,which represents the motion blur length.This length is used to establish a model correlating motion blur with axial velocity,allowing precise velocity calculation.Field tests in a hydropower station penstock demonstrated an average velocity error of 0.048 m/s compared to ultra-wideband(UWB)measurements.The root-mean-square error was 0.025,with an average computational time of 42.3 ms and CPU load of 17%.These results confirm the stability and accuracy of our velocity estimation algorithm in challenging environments.展开更多
In this paper, we will be looking at our efforts to find a novel solution for motion deblurring in videos. In addition, our solution has the requirement of being camera-independent. This means that the solution is ful...In this paper, we will be looking at our efforts to find a novel solution for motion deblurring in videos. In addition, our solution has the requirement of being camera-independent. This means that the solution is fully implemented in software and is not aware of any of the characteristics of the camera. We found a solution by implementing a Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) hybrid model. Our CNN-LSTM is able to deblur video without any knowledge of the camera hardware. This allows it to be implemented on any system that allows the camera to be swapped out with any camera model with any physical characteristics.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
Existing frequency-domain-oriented methods of parameter identification for uniform linear motion blur （ULMB） images usually dealt with special scenarios. For example, blur-kernel directions were horizontal or vertic...Existing frequency-domain-oriented methods of parameter identification for uniform linear motion blur （ULMB） images usually dealt with special scenarios. For example, blur-kernel directions were horizontal or vertical, or degraded images were of foursquare dimension. This excludes those identification methods from being applied to real images, especially to estimate undersized or oversized blur kernels. Pointing against the limitations of blur-kernel identifications, discrete Fourier transform （DFT）-based blur-kernel estimation methods are proposed in this paper. We analyze in depth the Fourier frequency response of generalized ULMB kernels, demonstrate in detail its related phase form and properties thereof, and put forward the concept of quasi-cepstrum. On this basis, methods of estimating ULMB-kernel parameters using amplitude spectrum and quasi-cepstrum are presented, respectively. The quasi-cepstrum-oriented approach increases the identifiable blur-kernel length, up to a maximum of half the diagonal length of the image. Meanwhile, directing toward the image of undersized ULMB, an improved method based on quasi-cepstrum is presented, which ameliorates the identification quality of undersized ULMB kernels. The quasi-cepstrum-oriented approach popularizes and applies the simulation-experiment- focused DFT theory to the estimation of real ULMB images. Compared against the amplitude-spectrum-oriented method, the quasi-cepstrum-oriented approach is more convenient and robust, with lower identification errors and of better noiseimmunity.展开更多
A simulation method is proposed to predict the motion artifacts of plasma display panels （PDPs）. The method simulates the behavior of the human vision system when perceiving moving objects. The simulation is based o...A simulation method is proposed to predict the motion artifacts of plasma display panels （PDPs）. The method simulates the behavior of the human vision system when perceiving moving objects. The simulation is based on the measured temporal light properties of the display for each gray level and each phosphor. Both the effect of subfield arrangement and phosphor decay are involved. A novel algorithm is proposed to improve the calculation speed. The simulation model manages to predict the appearance of the motion image perceived by a human with a still image. The results are validated by a set of perceptual evaluation experiments. This rapid and accurate prediction of motion artifacts enables objective characterization of the PDP performance in this aspect.展开更多
A novel motion-blur-based method for measuring the angular amplitude of a high-frequency rotational vibration is schemed.The proposed approach combines the active vision concept and the mechanism of motion-from-blur,g...A novel motion-blur-based method for measuring the angular amplitude of a high-frequency rotational vibration is schemed.The proposed approach combines the active vision concept and the mechanism of motion-from-blur,generates motion blur on the image plane actively by extending exposure time,and utilizes the motion blur information in polar images to estimate the angular amplitude of a high-frequency rotational vibration.This method obtains the analytical results of the angular vibration amplitude from the geometric moments of a motion blurred polar image and an unblurred image for reference.Experimental results are provided to validate the presented scheme.展开更多
The energy distribution model of motion blurred star point is analyzed.The distribution of the star point approximates to a two-dimensional(2 D) Gaussian distribution under degeneration.Two multi-parameter nonlinear G...The energy distribution model of motion blurred star point is analyzed.The distribution of the star point approximates to a two-dimensional(2 D) Gaussian distribution under degeneration.Two multi-parameter nonlinear Gaussian fitting methods(GFMs) are proposed,and the relationship between fitting parameters and motion blur parameters is analyzed.Estimation of the parameters of motion blur by fitting parameters is calculated to realize the error compensation of the motion blur.The simulation results show the effectiveness and accuracy.展开更多
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.
文摘Following publication of the original article[1],the statement of Data availability and Competing interests have been added.Data availability The datasets used and analyzed during this study are available from the corresponding author upon reasonable request.
基金The research was financially supported by Chongqing Science and Technology Commission Projects under Grant No.cstc2013yykfA80015 and Grant No.cstc2017shms-xdny80080Fundamental Research Funds for the Central Universities under Grant No.XDJK2016A007,XDJK2018D011Doctoral Scientific Research Foundation of Southwest University Project Grant No.SWU114109.
文摘As for machine vision-based intelligent system in the application of discriminating and sorting the sex of silkworm pupae,the tail gonad was the unique physiological feature.However,motion blur,resulting from the live silkworm pupa’s writhing motion at the moment of capturing image,could lose textures and structures(such as edge and tail gonad etc.)dramatically,which casted great challenges for sex identification.To increase the image quality and relieve the difficulty of discrimination caused by motion blur,an effective approach that including three stages was proposed in this work.In the image prediction stage,first sharp edges were acquired by using filtering techniques.Then the initial blur kernel was computed with Gaussian prior.The coarse version latent image was deconvoluted in the Fourier domain.In the kernel refinement stage,the Radon transform was applied to estimate the accurate kernel.In the final restoration step,a TV-L1 deconvolution model was carried out to obtain a better result.The experimental results showed that benefiting from the prediction step and kernel refinement step,the kernel was more accurate and the recovered image contained much more textures.It revealed that the proposed method was useful in removing the motion blur.Furthermore,the method could also be applied to other fields.
基金supported by Shanghai Municipal Science and Technology Major Project under Grant No.2021SHZDZX0100.
文摘Accurate velocity measurement of unmanned aerial vehicles(UAVs)is essential in various applications.Traditional vision-based methods rely heavily on visual features,which are often inadequate in low-light or feature-sparse environments.This study presents a novel approach to measure the axial velocity of UAVs using motion blur images captured by a UAV-mounted monocular camera.We introduce a motion blur model that synthesizes imaging from neighboring frames to enhance motion blur visibility.The synthesized blur frames are transformed into spectrograms using the Fast Fourier Transform(FFT)technique.We then apply a binarization process and the Radon transform to extract light-dark stripe spacing,which represents the motion blur length.This length is used to establish a model correlating motion blur with axial velocity,allowing precise velocity calculation.Field tests in a hydropower station penstock demonstrated an average velocity error of 0.048 m/s compared to ultra-wideband(UWB)measurements.The root-mean-square error was 0.025,with an average computational time of 42.3 ms and CPU load of 17%.These results confirm the stability and accuracy of our velocity estimation algorithm in challenging environments.
文摘In this paper, we will be looking at our efforts to find a novel solution for motion deblurring in videos. In addition, our solution has the requirement of being camera-independent. This means that the solution is fully implemented in software and is not aware of any of the characteristics of the camera. We found a solution by implementing a Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) hybrid model. Our CNN-LSTM is able to deblur video without any knowledge of the camera hardware. This allows it to be implemented on any system that allows the camera to be swapped out with any camera model with any physical characteristics.
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 in part by the National Natural Science Foundation of China under Grant Nos. 61032007, 60972126 and 60921061the Joint Funds of the National Natural Science Foundation of China under Grant No. U0935002/L05the Natural Science Foundation of Beijing under Grant No. 4102060
文摘Existing frequency-domain-oriented methods of parameter identification for uniform linear motion blur （ULMB） images usually dealt with special scenarios. For example, blur-kernel directions were horizontal or vertical, or degraded images were of foursquare dimension. This excludes those identification methods from being applied to real images, especially to estimate undersized or oversized blur kernels. Pointing against the limitations of blur-kernel identifications, discrete Fourier transform （DFT）-based blur-kernel estimation methods are proposed in this paper. We analyze in depth the Fourier frequency response of generalized ULMB kernels, demonstrate in detail its related phase form and properties thereof, and put forward the concept of quasi-cepstrum. On this basis, methods of estimating ULMB-kernel parameters using amplitude spectrum and quasi-cepstrum are presented, respectively. The quasi-cepstrum-oriented approach increases the identifiable blur-kernel length, up to a maximum of half the diagonal length of the image. Meanwhile, directing toward the image of undersized ULMB, an improved method based on quasi-cepstrum is presented, which ameliorates the identification quality of undersized ULMB kernels. The quasi-cepstrum-oriented approach popularizes and applies the simulation-experiment- focused DFT theory to the estimation of real ULMB images. Compared against the amplitude-spectrum-oriented method, the quasi-cepstrum-oriented approach is more convenient and robust, with lower identification errors and of better noiseimmunity.
文摘A simulation method is proposed to predict the motion artifacts of plasma display panels （PDPs）. The method simulates the behavior of the human vision system when perceiving moving objects. The simulation is based on the measured temporal light properties of the display for each gray level and each phosphor. Both the effect of subfield arrangement and phosphor decay are involved. A novel algorithm is proposed to improve the calculation speed. The simulation model manages to predict the appearance of the motion image perceived by a human with a still image. The results are validated by a set of perceptual evaluation experiments. This rapid and accurate prediction of motion artifacts enables objective characterization of the PDP performance in this aspect.
基金supported by National Natural Science Foundation of China(No.50375099,No.50390064)
文摘A novel motion-blur-based method for measuring the angular amplitude of a high-frequency rotational vibration is schemed.The proposed approach combines the active vision concept and the mechanism of motion-from-blur,generates motion blur on the image plane actively by extending exposure time,and utilizes the motion blur information in polar images to estimate the angular amplitude of a high-frequency rotational vibration.This method obtains the analytical results of the angular vibration amplitude from the geometric moments of a motion blurred polar image and an unblurred image for reference.Experimental results are provided to validate the presented scheme.
文摘The energy distribution model of motion blurred star point is analyzed.The distribution of the star point approximates to a two-dimensional(2 D) Gaussian distribution under degeneration.Two multi-parameter nonlinear Gaussian fitting methods(GFMs) are proposed,and the relationship between fitting parameters and motion blur parameters is analyzed.Estimation of the parameters of motion blur by fitting parameters is calculated to realize the error compensation of the motion blur.The simulation results show the effectiveness and accuracy.
