In this paper , we incorporate an underwater deblurring method for the minimization of an energy function . The incorporated method uses ill-posed and sparse modeling for deblurring . The blur modeling is done for typical underwater turbidity which can be restored through the proposed algorithm . We present a fast , platelet-based maximum penalized likelihood algorithm that encompasses denoising , deblurring , and tomographic reconstruction and its applications to photon-limited imaging . Platelet-based estimation techniques are applied to nuclear medicine data for denoising and confocal microscopy data for deblurring . Blind motion deblurring is an important problem that receives enduring attention in last decade . Based on the observation that a good intermediate estimate of latent image for estimating motion-blur kernel is not necessarily the one closest to latent image , edge selection has proven itself a very powerful technique for achieving state-of-the-art performance in blind deblurring . This paper presented an interpretation of edge selection/reweighting in terms of variational Bayes inference , and therefore developed a novel variational expectation maximization ( VEM ) algorithm with built-in adaptive edge selection for blind deblurring . The effectiveness of the proposed blur kernel estimation method is enhanced by incorporating both the L1-norm of kernel intensity and the squared L2-norm of the intensity derivative . Once the accurate estimation of the blur kernel is obtained , the original blind deblurring can be simplified to the direct deconvolution of blurred images . To guarantee robust non-blind deconvolution , a variational image restoration model is presented based on the L1-norm data-fidelity term and the total generalized variation ( TGV ) regularizer of second-order . An augmented Lagrangian method is used to handle the constraints , and an alternating direction method ( ADM ) is used to iteratively find solutions of the subproblems . The proposed algorithm has a wide range of applications , including video deblurring and denoising , disparity map refinement , and reducing hot-air turbulence effects . Due to the relative motion between camera and scene to be photographed during exposure , image deblurring is commonly categorized into two types : space-invariant and space-variant versions . Space-variant image deblurring is more difficult since the blurring effect can not be modeled by convolution . In this paper , we mainly focus on the problem of space-variant text image deblurring . The blur kernel for each kernel cluster is finally refined using the corresponding image region , which is the union of image patches associated with the kernels in the cluster . For space-variant image deblurring , the entire image is deconvolved with each blur kernel to produce a set of deblurred images . These images are then fused to produce a blur-free image , where the fusion process selects the optimal regions from the set of deblurred images . An optimization theoretic formulation for sparse solutions is presented , and its relationship to the MUSIC algorithm is discussed . Two algorithms for sparse inverse problems are introduced , and examples of their application to beamforming array design and star image deblurring are presented . Since the blurring can be assumed as a two-dimensional convolution in the y x − plane between the 3-D data and the point spread function ( PSF ) , the acquired B-scan data ) , , ( z y x g can be represented as [ 9,10 ] : ) , , ( ) , , ( ) , , ( ) , , ( z y x n z y x h z y x f z y x g + ∗ = ( 1 ) where ) , , ( z y x f , ) , , ( z y x h and ) , , ( z y x n denote the desired 3-D data of the specimen , the PSF of the ultrasonic beam and the inherent measurement noise , respectively . The deblurring of B-scan data is realized through Wiener filtering . Wiener filtering produces ) , , ( ˆ z y x f that estimates original 3-D shape of specimen ) , , ( z y x f from ) , , ( z y x g . ) , , ( ˆ z y x f minimizes the mean-squared error between ) , , ( z y x f and ) , , ( ˆ z y x f [ 11 ] . In this thesis it is shown that analog neural networks provide an elegant solution to some of the problems of low level tactile data processing . We consider the particular problem of ` deblurring ' strain data from an array of tactile sensors . It is shown that the inverse problem of deblurring strain measurements to recover the surface stress over a region of contact is ill-posed in the sense defined by Hadamard . One method to ameliorate this effect is image deconvolution using a point spread function ( PSF ) based on an estimation of the luminescent time constant . Prior implementations of image deconvolution for PSP deblurring have relied upon a spatially invariant time constant in order to reduce computational time . However , the use of an assumed value of time constant leads to errors in the point spread function , particularly when strong pressure gradients ( which cause strong spatial gradients in the decay time constant ) are involved . Second , an effective underwater image degradation model is constructed to model the statistics of the scattering event . Then , the underwater image deblurring is achieved using a group-based sparse representation method . To evaluate the performance of the proposed method , we compare our results with several existing approaches using the subjective technique as well as the objective technique . To improve the SNR , cameras are designed with near optimal quantum efficiency and maximum fill-factor . However , the latter widens the system PSF , which puts more weight on the deblurring part of a super-resolution ( SR ) reconstruction algorithm . This paper analyzes the contribution of each input parameters to the SR reconstruction and predicts the best attainable SR factor for given a camera setting . There have been a few studies of using deep learning techniques for image restoration . The multi - layer perceptron ( MLP ) , whose all layers are fully - connected ( in contrast to convolutional ) , is applied for natural image denoising [ reference ] and post - deblurring denoising [ reference ] . More closely related to our work , the convolutional neural network is applied for natural image denoising [ reference ] and removing noisy patterns ( dirt / rain ) [ reference ] . These restoration problems are more or less denoising - driven . We conjecture that additional performance can be further gained by exploring more filters and different training strategies . Besides , the proposed structure , with its advantages of simplicity and robustness , could be applied to other low - level vision problems , such as image deblurring or simultaneous SR + denoising . One could also investigate a network to cope with different upscaling factors . Scientific applications such as microscopy or astronomy , which push the fundamental physical limitations of light , lenses and sensors , face similar challenges . Recovering high - quality images from degraded measurements has been a long - standing problem for image analysis and spans a range of tasks such as blind - image deblurring , compression artifact reduction , and single image super - resolution . Such image reconstruction tasks can be viewed mathematically as inverse problems , which are typically ill - posed and massively under - constrained . We attribute our better performance to two reasons . First , our CNN model learns a direct inverse mapping from blurry patch to its clear counterpart based on the learned image distribution , whereas only estimates the blur kernel for the patch and uses an offline optimization for non - blind deblurring , resulting in some artifacts such as ringing . Second , our CNN architecture is higher fidelity than the one used in , as ours outputs full - resolution result and learns internally to minimize artifacts , e.g. , aliasing and ringing effect . subsection : JPEG Compression Artifact Reduction Similar to training for image deblurring , we generate JPEG compressed image patches from original non - compressed ones on the fly during training . This can be easily done using JPEG compression function by varying the quality factor ( QF ) of interest . Another practical degradation model can be given by When is the bicubic downsampler , Eqn . ( [ reference ] ) corresponds to a deblurring problem followed by a SISR problem with bicubic degradation . Thus , it can benefit from existing deblurring methods and bicubic degradation based SISR methods . In , anisotropic Gaussian blur kernels are also used . In practice , more complex blur kernel models used in deblurring task , such as motion blur , can be further considered . Empirical and theoretical analyses have revealed that the influence of an accurate blur kernel is much larger than that of sophisticated image priors . Significantly , with the large capacity , we show it is possible to cope with different levels of corruptions using a single model . Using the same framework , we train models on tasks of image denoising , super resolution removing JPEG compression artifacts , non - blind image deblurring and image inpainting . Our experiment results on benchmark datasets show that our network can achieve best reported performance on all of the four tasks , and set new state - of - the - art . A neat conclusion of this result is that modulation of a bandlimited signal protects it against an unknown convolutive distortion . We discuss the applications of this result in passive imaging , wireless communication in unknown environment , and image deblurring . A thorough numerical investigation of the theoretical results is also presented using phase transitions , image deblurring experiments , and noise stability plots . Blind image deblurring , i.e. , deblurring without knowledge of the blur kernel , is a highly ill-posed problem . The problem can be solved in two parts : i ) estimate a blur kernel from the blurry image , and ii ) given estimated blur kernel , de-convolve blurry input to restore the target image . In this paper , we find that the blur process changes the similarity of neighboring image patches . Based on the intriguing observation , we show how to effectively apply the low rank prior to blind image deblurring and present a new algorithm that combines low rank prior and salient edge selection . The low rank prior provides data-authentic prior for the intermediate latent image restoration , while salient edges provide reliable edge information for kernel estimation . In hybrid sensor networks , information fusion from heterogeneous sensors is important , but quite often information such as image is blurred . Single image deblurring is a highly ill-posed problem and usually regularized by alternating estimating point spread function ( PSF ) and recovering blur image , which leads to high complexity and low efficiency . In this paper , we first propose an efficient PSF estimation algorithm based on gradient cepstrum analysis ( GCA ) . The goal of single image deblurring is to recover both a latent clear image and an underlying blur kernel from one input blurred image . Recent works focus on exploiting natural image priors or additional image observations for deblurring , but pay less attention to the influence of image structures on estimating blur kernels . We simplified the model to 3-dimensional camera motion which can be accelerated by exploiting the fast Fourier transform to process subsequent image deconvolution . We then proposed an effective method to handle the blind image-deblurring problem by the image decomposition , which does not need to segment the image into local subregions under the assumption of spatially invariant blur . Experimental results on both synthetic and real blurred images show that the presented approach can successfully remove various kinds of blur . The method only makes weak assumptions about the blurring filter and is able to undo a wide variety of blurring degradations . To overcome the ill-posedness of the blind image deblurring problem , the method includes a learning technique which initially focuses on the main edges of the image and gradually takes details into account . A new image prior , which includes a new edge detector , is used . This leads to a graph spectral filtering interpretation of the prior with desirable properties , including robustness to noise and blur , strong piecewise smooth filtering , and sharpness promotion . Minimizing a blind image deblurring objective with RGTV results in a non-convex non-differentiable optimization problem . Leveraging the new graph spectral interpretation for RGTV , we design an efficient algorithm that solves for the skeleton image and the blur kernel alternately . Multi-scale ( MS ) approaches have been widely investigated for blind single image / video deblurring that sequentially recovers deblurred images in low spatial scale first and then in high spatial scale later with the output of lower scales . MS approaches have been effective especially for severe blurs induced by large motions in high spatial scale since those can be seen as small blurs in low spatial scale . Due to the oscillatory walking nature of these platforms , the cameras on them experience disturbances so that most of the captured frames are motion blurred . Motion blur results in loss of information in individual image frames and therefore makes single-frame deblurring an ill-posed problem . The variation in the magnitude of motion blur in consecutive video frames can be exploited to better restore these frames by combining the information available in them . Interestingly , they showed that VDSR can handle multiple scales super - resolution . By analyzing the relation between CNN and MAP inference , Zhang pointed out that CNN mainly model the prior information and they empirically demonstrated that a single model can handle multiple scales super - resolution , image deblocking and image denoising . While achieving good performance , the above methods take the bicubicly interpolated LR image as input , which not only suffers from high computational cost but also hinders the effective expansion of receptive field . Third , we study the correlation between human subjective scores and several full-reference and noreference image quality metrics . Our evaluation and analysis indicate the performance gap between synthetically blurred images and real blurred image and sheds light on future research in single image blind deblurring . Abstract Blind image deblurring aims to recover the sharp image from a blurred observation , which is an ill-posed inverse problem . Proper image priors for the unknown variables ( i.e. latent sharp image and blur kernel ) are crucial . This approach generates a multi-resolution pyramid of the input blurred image and propagates the estimation results of the lower resolution levels to the higher resolution ones . By considering the PSF initiation as an important step in the blind deblurring process , we introduce a PSF initiation method that benefits from the salient structure obtained via an l0-gradient smoothing of blurred image . A first-order primal-dual solution to the l0-gradient smoothing problem is introduced in this paper . This encoding embeds both the motion direction and extent by coloring the spatial blur of each object . The color cues serve as prior information for a blind deblurring process , implemented using a convolutional neural network ( CNN ) trained to utilize such coding for image restoration . We demonstrate the advantage of the proposed approach over blind-deblurring with no coding and other solutions that use coded acquisition , both in simulation and real-world experiments . The blind deblurring is a more challenge problem because we need to recover the true image but the blur type is unknown . The total variation method has been proposed by Tony Chan et al. [ 1 ] for image blind deblurring , but it often produces the staircase effect . To avoid this effect , in this paper , based on the fractional-order differential , we propose a novel variational method for blind deblurring . Images obtained from unconstrained environments may be blurred by unknown kernels and affected due to noise . This paper presents a new total variation minimization-based method for blindly deblurring such images . Unlike the alternating optimization-based algorithms , the proposed algorithm adopts a joint estimation strategy to estimate the unknown blurring kernel and the unknown image in an iterative manner , where each iteration performs two separate image denoising subproblems that admit fast implementation .
In this paper , we incorporate an underwater deblurring method for the minimization of an energy function . The incorporated method uses ill-posed and sparse modeling for deblurring . The blur modeling is done for typical underwater turbidity which can be restored through the proposed algorithm .
We present a fast , platelet-based maximum penalized likelihood algorithm that encompasses denoising , deblurring , and tomographic reconstruction and its applications to photon-limited imaging . Platelet-based estimation techniques are applied to nuclear medicine data for denoising and confocal microscopy data for deblurring .
Blind motion deblurring is an important problem that receives enduring attention in last decade . Based on the observation that a good intermediate estimate of latent image for estimating motion-blur kernel is not necessarily the one closest to latent image , edge selection has proven itself a very powerful technique for achieving state-of-the-art performance in blind deblurring . This paper presented an interpretation of edge selection/reweighting in terms of variational Bayes inference , and therefore developed a novel variational expectation maximization ( VEM ) algorithm with built-in adaptive edge selection for blind deblurring .
The effectiveness of the proposed blur kernel estimation method is enhanced by incorporating both the L1-norm of kernel intensity and the squared L2-norm of the intensity derivative . Once the accurate estimation of the blur kernel is obtained , the original blind deblurring can be simplified to the direct deconvolution of blurred images . To guarantee robust non-blind deconvolution , a variational image restoration model is presented based on the L1-norm data-fidelity term and the total generalized variation ( TGV ) regularizer of second-order .
An augmented Lagrangian method is used to handle the constraints , and an alternating direction method ( ADM ) is used to iteratively find solutions of the subproblems . The proposed algorithm has a wide range of applications , including video deblurring and denoising , disparity map refinement , and reducing hot-air turbulence effects .
Due to the relative motion between camera and scene to be photographed during exposure , image deblurring is commonly categorized into two types : space-invariant and space-variant versions . Space-variant image deblurring is more difficult since the blurring effect can not be modeled by convolution . In this paper , we mainly focus on the problem of space-variant text image deblurring .
The blur kernel for each kernel cluster is finally refined using the corresponding image region , which is the union of image patches associated with the kernels in the cluster . For space-variant image deblurring , the entire image is deconvolved with each blur kernel to produce a set of deblurred images . These images are then fused to produce a blur-free image , where the fusion process selects the optimal regions from the set of deblurred images .
An optimization theoretic formulation for sparse solutions is presented , and its relationship to the MUSIC algorithm is discussed . Two algorithms for sparse inverse problems are introduced , and examples of their application to beamforming array design and star image deblurring are presented .
Since the blurring can be assumed as a two-dimensional convolution in the y x − plane between the 3-D data and the point spread function ( PSF ) , the acquired B-scan data ) , , ( z y x g can be represented as [ 9,10 ] : ) , , ( ) , , ( ) , , ( ) , , ( z y x n z y x h z y x f z y x g + ∗ = ( 1 ) where ) , , ( z y x f , ) , , ( z y x h and ) , , ( z y x n denote the desired 3-D data of the specimen , the PSF of the ultrasonic beam and the inherent measurement noise , respectively . The deblurring of B-scan data is realized through Wiener filtering . Wiener filtering produces ) , , ( ˆ z y x f that estimates original 3-D shape of specimen ) , , ( z y x f from ) , , ( z y x g . ) , , ( ˆ z y x f minimizes the mean-squared error between ) , , ( z y x f and ) , , ( ˆ z y x f [ 11 ] .
In this thesis it is shown that analog neural networks provide an elegant solution to some of the problems of low level tactile data processing . We consider the particular problem of ` deblurring ' strain data from an array of tactile sensors . It is shown that the inverse problem of deblurring strain measurements to recover the surface stress over a region of contact is ill-posed in the sense defined by Hadamard .
One method to ameliorate this effect is image deconvolution using a point spread function ( PSF ) based on an estimation of the luminescent time constant . Prior implementations of image deconvolution for PSP deblurring have relied upon a spatially invariant time constant in order to reduce computational time . However , the use of an assumed value of time constant leads to errors in the point spread function , particularly when strong pressure gradients ( which cause strong spatial gradients in the decay time constant ) are involved .
Second , an effective underwater image degradation model is constructed to model the statistics of the scattering event . Then , the underwater image deblurring is achieved using a group-based sparse representation method . To evaluate the performance of the proposed method , we compare our results with several existing approaches using the subjective technique as well as the objective technique .
To improve the SNR , cameras are designed with near optimal quantum efficiency and maximum fill-factor . However , the latter widens the system PSF , which puts more weight on the deblurring part of a super-resolution ( SR ) reconstruction algorithm . This paper analyzes the contribution of each input parameters to the SR reconstruction and predicts the best attainable SR factor for given a camera setting .
There have been a few studies of using deep learning techniques for image restoration . The multi - layer perceptron ( MLP ) , whose all layers are fully - connected ( in contrast to convolutional ) , is applied for natural image denoising [ reference ] and post - deblurring denoising [ reference ] . More closely related to our work , the convolutional neural network is applied for natural image denoising [ reference ] and removing noisy patterns ( dirt / rain ) [ reference ] . These restoration problems are more or less denoising - driven .
We conjecture that additional performance can be further gained by exploring more filters and different training strategies . Besides , the proposed structure , with its advantages of simplicity and robustness , could be applied to other low - level vision problems , such as image deblurring or simultaneous SR + denoising . One could also investigate a network to cope with different upscaling factors .
Scientific applications such as microscopy or astronomy , which push the fundamental physical limitations of light , lenses and sensors , face similar challenges . Recovering high - quality images from degraded measurements has been a long - standing problem for image analysis and spans a range of tasks such as blind - image deblurring , compression artifact reduction , and single image super - resolution . Such image reconstruction tasks can be viewed mathematically as inverse problems , which are typically ill - posed and massively under - constrained .
We attribute our better performance to two reasons . First , our CNN model learns a direct inverse mapping from blurry patch to its clear counterpart based on the learned image distribution , whereas only estimates the blur kernel for the patch and uses an offline optimization for non - blind deblurring , resulting in some artifacts such as ringing . Second , our CNN architecture is higher fidelity than the one used in , as ours outputs full - resolution result and learns internally to minimize artifacts , e.g. , aliasing and ringing effect .
subsection : JPEG Compression Artifact Reduction Similar to training for image deblurring , we generate JPEG compressed image patches from original non - compressed ones on the fly during training . This can be easily done using JPEG compression function by varying the quality factor ( QF ) of interest .
Another practical degradation model can be given by When is the bicubic downsampler , Eqn . ( [ reference ] ) corresponds to a deblurring problem followed by a SISR problem with bicubic degradation . Thus , it can benefit from existing deblurring methods and bicubic degradation based SISR methods .
In , anisotropic Gaussian blur kernels are also used . In practice , more complex blur kernel models used in deblurring task , such as motion blur , can be further considered . Empirical and theoretical analyses have revealed that the influence of an accurate blur kernel is much larger than that of sophisticated image priors .
Significantly , with the large capacity , we show it is possible to cope with different levels of corruptions using a single model . Using the same framework , we train models on tasks of image denoising , super resolution removing JPEG compression artifacts , non - blind image deblurring and image inpainting . Our experiment results on benchmark datasets show that our network can achieve best reported performance on all of the four tasks , and set new state - of - the - art .
A neat conclusion of this result is that modulation of a bandlimited signal protects it against an unknown convolutive distortion . We discuss the applications of this result in passive imaging , wireless communication in unknown environment , and image deblurring . A thorough numerical investigation of the theoretical results is also presented using phase transitions , image deblurring experiments , and noise stability plots .
Blind image deblurring , i.e. , deblurring without knowledge of the blur kernel , is a highly ill-posed problem . The problem can be solved in two parts : i ) estimate a blur kernel from the blurry image , and ii ) given estimated blur kernel , de-convolve blurry input to restore the target image .
In this paper , we find that the blur process changes the similarity of neighboring image patches . Based on the intriguing observation , we show how to effectively apply the low rank prior to blind image deblurring and present a new algorithm that combines low rank prior and salient edge selection . The low rank prior provides data-authentic prior for the intermediate latent image restoration , while salient edges provide reliable edge information for kernel estimation .
In hybrid sensor networks , information fusion from heterogeneous sensors is important , but quite often information such as image is blurred . Single image deblurring is a highly ill-posed problem and usually regularized by alternating estimating point spread function ( PSF ) and recovering blur image , which leads to high complexity and low efficiency . In this paper , we first propose an efficient PSF estimation algorithm based on gradient cepstrum analysis ( GCA ) .
The goal of single image deblurring is to recover both a latent clear image and an underlying blur kernel from one input blurred image . Recent works focus on exploiting natural image priors or additional image observations for deblurring , but pay less attention to the influence of image structures on estimating blur kernels .
We simplified the model to 3-dimensional camera motion which can be accelerated by exploiting the fast Fourier transform to process subsequent image deconvolution . We then proposed an effective method to handle the blind image-deblurring problem by the image decomposition , which does not need to segment the image into local subregions under the assumption of spatially invariant blur . Experimental results on both synthetic and real blurred images show that the presented approach can successfully remove various kinds of blur .
The method only makes weak assumptions about the blurring filter and is able to undo a wide variety of blurring degradations . To overcome the ill-posedness of the blind image deblurring problem , the method includes a learning technique which initially focuses on the main edges of the image and gradually takes details into account . A new image prior , which includes a new edge detector , is used .
This leads to a graph spectral filtering interpretation of the prior with desirable properties , including robustness to noise and blur , strong piecewise smooth filtering , and sharpness promotion . Minimizing a blind image deblurring objective with RGTV results in a non-convex non-differentiable optimization problem . Leveraging the new graph spectral interpretation for RGTV , we design an efficient algorithm that solves for the skeleton image and the blur kernel alternately .
Multi-scale ( MS ) approaches have been widely investigated for blind single image / video deblurring that sequentially recovers deblurred images in low spatial scale first and then in high spatial scale later with the output of lower scales . MS approaches have been effective especially for severe blurs induced by large motions in high spatial scale since those can be seen as small blurs in low spatial scale .
Due to the oscillatory walking nature of these platforms , the cameras on them experience disturbances so that most of the captured frames are motion blurred . Motion blur results in loss of information in individual image frames and therefore makes single-frame deblurring an ill-posed problem . The variation in the magnitude of motion blur in consecutive video frames can be exploited to better restore these frames by combining the information available in them .
Interestingly , they showed that VDSR can handle multiple scales super - resolution . By analyzing the relation between CNN and MAP inference , Zhang pointed out that CNN mainly model the prior information and they empirically demonstrated that a single model can handle multiple scales super - resolution , image deblocking and image denoising . While achieving good performance , the above methods take the bicubicly interpolated LR image as input , which not only suffers from high computational cost but also hinders the effective expansion of receptive field .
Third , we study the correlation between human subjective scores and several full-reference and noreference image quality metrics . Our evaluation and analysis indicate the performance gap between synthetically blurred images and real blurred image and sheds light on future research in single image blind deblurring .
Abstract Blind image deblurring aims to recover the sharp image from a blurred observation , which is an ill-posed inverse problem . Proper image priors for the unknown variables ( i.e. latent sharp image and blur kernel ) are crucial .
This approach generates a multi-resolution pyramid of the input blurred image and propagates the estimation results of the lower resolution levels to the higher resolution ones . By considering the PSF initiation as an important step in the blind deblurring process , we introduce a PSF initiation method that benefits from the salient structure obtained via an l0-gradient smoothing of blurred image . A first-order primal-dual solution to the l0-gradient smoothing problem is introduced in this paper .
This encoding embeds both the motion direction and extent by coloring the spatial blur of each object . The color cues serve as prior information for a blind deblurring process , implemented using a convolutional neural network ( CNN ) trained to utilize such coding for image restoration . We demonstrate the advantage of the proposed approach over blind-deblurring with no coding and other solutions that use coded acquisition , both in simulation and real-world experiments .
The blind deblurring is a more challenge problem because we need to recover the true image but the blur type is unknown . The total variation method has been proposed by Tony Chan et al. [ 1 ] for image blind deblurring , but it often produces the staircase effect . To avoid this effect , in this paper , based on the fractional-order differential , we propose a novel variational method for blind deblurring .
Images obtained from unconstrained environments may be blurred by unknown kernels and affected due to noise . This paper presents a new total variation minimization-based method for blindly deblurring such images . Unlike the alternating optimization-based algorithms , the proposed algorithm adopts a joint estimation strategy to estimate the unknown blurring kernel and the unknown image in an iterative manner , where each iteration performs two separate image denoising subproblems that admit fast implementation .
Underwater turbidity removal through ill-posed optimization with sparse modeling Platelets for multiscale analysis in medical imaging A Variational EM Framework With Adaptive Edge Selection for Blind Motion Deblurring Effective Alternating Direction Optimization Methods for Sparsity-Constrained Blind Image Deblurring An augmented Lagrangian method for video restoration Space-variant text image deblurring with nonconvex constraint Space-variant blur kernel estimation and image deblurring through kernel clustering Sparse inverse solution methods for signal and image processing applications Generation of Three-Dimensional Images Using Ultrasonic Pulse-Echo Data for Fault Characterization Neural Networks for Low Level Processing of Tactile Sensory Data Iterative Blind Deconvolution Algorithm for Deblurring a Single PSP/TSP Image of Rotating Surfaces Integrative framework for the effective restoration of underwater images Influence of signal-to-noise ratio and point spread function on limits of superresolution Image Super-Resolution Using Deep Convolutional Networks Image Reconstruction with Predictive Filter Flow Learning a Single Convolutional Super-Resolution Network for Multiple Degradations Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections Blind Deconvolution Using Modulated Inputs Blind Image Deblurring Via Reweighted Graph Total Variation Blur kernel estimation via salient edges and low rank prior for blind image deblurring PSF Estimation via Gradient Cepstrum Analysis for Image Deblurring in Hybrid Sensor Network Good Regions to Deblur Single-Image Blind Deblurring for Non-uniform Camera-Shake Blur Blind and Semi-Blind Deblurring of Natural Images Graph-Based Blind Image Deblurring From a Single Photograph Multi-Temporal Recurrent Neural Networks For Progressive Non-Uniform Single Image Deblurring With Incremental Temporal Training Multi-Frame motion deblurring of video using the natural oscillatory motion of dexterous legged robots Learning a Single Convolutional Super-Resolution Network for Multiple Degradations A Comparative Study for Single Image Blind Deblurring Blind image deblurring by promoting group sparsity Blind image deblurring based on multi-resolution ringing removal Motion Deblurring using Spatiotemporal Phase Aperture Coding A Fractional-order Differential Based Variational Model for Image Blind Deblurring Fast Weighted Total Variation Regularization Algorithm for Blur Identification and Image Restoration
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CVPR ICASSP ICASSP Image Processing: Algorithms and Systems IEEE Transactions on Pattern Analysis and Machine Intelligence ArXiv 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ArXiv ECCV (5) ACCV (3) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition CVPR
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