The Experts below are selected from a list of 4572 Experts worldwide ranked by ideXlab platform
Yanning Zhang - One of the best experts on this subject based on the ideXlab platform.
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Robust Motion Blur Kernel Estimation by Kernel Continuity Prior
IEEE Access, 2020Co-Authors: Xueling Chen, Yanning ZhangAbstract:The accurate Kernel Estimation is key to the blind motion deblurring. Many previous methods depend on the image regularization to recover strong edges in the observed image for Kernel Estimation. However, the estimated Kernel will be degraded when recovered strong edges are less accurate, especially in images full of small-scale edges. Different from previous methods, we focus on the Kernel regularization. Inspired by the fact that the blur Kernel is highly related to the continuous camera motion trajectory during the image capturing, we propose to encourage the continuity of the Kernel through a Kernel prior. The proposed prior measures the continuity of each element in the Kernel and generates a continuity map. By encouraging the sparsity of the map using L0 norm, discontinuous Kernel elements are suppressed. Since the model with the proposed prior is non-convex and non-linear, an approximation method is proposed to minimize the cost function efficiently. Numerous experimental results show that our method outperforms state-of-the-art methods on both the normal and challenging cases. Moreover, the proposed prior is able to further improve the performance of existing MAP-based methods.
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Parametric model for image blur Kernel Estimation
2018 International Conference on Orange Technologies (ICOT), 2018Co-Authors: Ao Zhang, Min Wang, Yanning ZhangAbstract:This paper we propose an novel parametric approach for single image Kernel Estimation with both motion blur and Gaussian blur coupled. In the view of that daily pictures captured by handheld device usually contain motion blur and defocus simultaneously. During one shot, the moving trail of the object can be always regarded as straight and consecutive, and the defocus phenomenon is related to Gaussian blur. Therefore, a parameter model containing three parameters can describe the blur. First, we estimate a rough blur Kernel using L1 prior method, then we fit the Kernel by computing the three parameters. Finally, the sharp image with clear details is restored by the Kernel estimated. Experimental results show that the proposed method outperforms others when the blur Kernel is fairly parameterized, which helps the current blind deconvolution methods achieve better results.
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ICCV - Self-Paced Kernel Estimation for Robust Blind Image Deblurring
2017 IEEE International Conference on Computer Vision (ICCV), 2017Co-Authors: Dong Gong, Yanning Zhang, Anton Van Den HengelAbstract:The challenge in blind image deblurring is to remove the effects of blur with limited prior information about the nature of the blur process. Existing methods often assume that the blur image is produced by linear convolution with additive Gaussian noise. However, including even a small number of outliers to this model in the Kernel Estimation process can significantly reduce the resulting image quality. Previous methods mainly rely on some simple but unreliable heuristics to identify outliers for Kernel Estimation. Rather than attempt to identify outliers to the model a priori, we instead propose to sequentially identify inliers, and gradually incorporate them into the Estimation process. The selfpaced Kernel Estimation scheme we propose represents a generalization of existing self-paced learning approaches, in which we gradually detect and include reliable inlier pixel sets in a blurred image for Kernel Estimation. Moreover, we automatically activate a subset of significant gradients w.r.t. the reliable inlier pixels, and then update the intermediate sharp image and the Kernel accordingly. Experiments on both synthetic data and real-world images with various kinds of outliers demonstrate the effectiveness and robustness of the proposed method compared to the stateof- the-art methods.
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Self-Paced Kernel Estimation for Robust Blind Image Deblurring
2017 IEEE International Conference on Computer Vision (ICCV), 2017Co-Authors: Dong Gong, Yanning Zhang, Anton Van Den HengelAbstract:The challenge in blind image deblurring is to remove the effects of blur with limited prior information about the nature of the blur process. Existing methods often assume that the blur image is produced by linear convolution with additive Gaussian noise. However, including even a small number of outliers to this model in the Kernel Estimation process can significantly reduce the resulting image quality. Previous methods mainly rely on some simple but unreliable heuristics to identify outliers for Kernel Estimation. Rather than attempt to identify outliers to the model a priori, we instead propose to sequentially identify inliers, and gradually incorporate them into the Estimation process. The self-paced Kernel Estimation scheme we propose represents a generalization of existing self-paced learning approaches, in which we gradually detect and include reliable inlier pixel sets in a blurred image for Kernel Estimation. Moreover, we automatically activate a subset of significant gradients w.r.t. the reliable inlier pixels, and then update the intermediate sharp image and the Kernel accordingly. Experiments on both synthetic data and real-world images with various kinds of outliers demonstrate the effectiveness and robustness of the proposed method compared to the stateof- the-art methods.
Zhixun Su - One of the best experts on this subject based on the ideXlab platform.
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Blur Kernel Estimation via salient edges and low rank prior for blind image deblurring
Signal Processing-image Communication, 2017Co-Authors: Jiangxin Dong, Zhixun SuAbstract:Blind image deblurring, i.e., estimating a blur Kernel from a single blurred image, is a severely ill-posed problem. 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. When estimating blur Kernels, salient edges are extracted from an intermediate latent image solved by combining the predicted edges and the low rank prior, which are able to remove tiny details and preserve sharp edges in the intermediate latent image Estimation thus facilitating blur Kernel Estimation. We analyze the effectiveness of the low rank prior in image deblurring and show that it is able to favor clear images over blurred ones. In addition, we show that the proposed method can be extended to non-uniform image deblurring. Extensive experiments demonstrate that the proposed method performs favorably against state-of-the-art algorithms, both qualitatively and quantitatively. We propose the low rank prior with salient edge selection for blind image deblurring.We analyze how the low rank prior helps blur Kernel Estimation in detail.We extend the proposed method and show its effectiveness on nonuniform deblurring.We discuss the limitations of the proposed algorithm.We evaluate our method on both synthetic and real-world images.
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Kernel Estimation from salient structure for robust motion deblurring
Signal Processing-image Communication, 2013Co-Authors: Zhixun Su, Xianfeng GuAbstract:Blind image deblurring algorithms have been improving steadily in the past years. Most state-of-the-art algorithms, however, still cannot perform perfectly in challenging cases, especially in large blur setting. In this paper, we focus on how to estimate a good blur Kernel from a single blurred image based on the image structure. We found that image details caused by blur could adversely affect the Kernel Estimation, especially when the blur Kernel is large. One effective way to remove these details is to apply image denoising model based on the total variation (TV). First, we developed a novel method for computing image structures based on the TV model, such that the structures undermining the Kernel Estimation will be removed. Second, we applied a gradient selection method to mitigate the possible adverse effect of salient edges and improve the robustness of Kernel Estimation. Third, we proposed a novel Kernel Estimation method, which is capable of removing noise and preserving the continuity in the Kernel. Finally, we developed an adaptive weighted spatial prior to preserve sharp edges in latent image restoration. Extensive experiments testify to the effectiveness of our method on various kinds of challenging examples.
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Fast $\ell ^{0}$-Regularized Kernel Estimation for Robust Motion Deblurring
IEEE Signal Processing Letters, 2013Co-Authors: Zhixun SuAbstract:Blind image deblurring is a challenging problem in computer vision and image processing. In this paper, we propose a new l0-regularized approach to estimate a blur Kernel from a single blurred image by regularizing the sparsity property of natural images. Furthermore, by introducing an adaptive structure map in the deblurring process, our method is able to restore useful salient edges for Kernel Estimation. Finally, we propose an efficient algorithm which can solve the proposed model efficiently. Extensive experiments compared with state-of-the-art blind deblurring methods demonstrate the effectiveness of the proposed method.
Xianfeng Gu - One of the best experts on this subject based on the ideXlab platform.
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Kernel Estimation from salient structure for robust motion deblurring
Signal Processing-image Communication, 2013Co-Authors: Zhixun Su, Xianfeng GuAbstract:Blind image deblurring algorithms have been improving steadily in the past years. Most state-of-the-art algorithms, however, still cannot perform perfectly in challenging cases, especially in large blur setting. In this paper, we focus on how to estimate a good blur Kernel from a single blurred image based on the image structure. We found that image details caused by blur could adversely affect the Kernel Estimation, especially when the blur Kernel is large. One effective way to remove these details is to apply image denoising model based on the total variation (TV). First, we developed a novel method for computing image structures based on the TV model, such that the structures undermining the Kernel Estimation will be removed. Second, we applied a gradient selection method to mitigate the possible adverse effect of salient edges and improve the robustness of Kernel Estimation. Third, we proposed a novel Kernel Estimation method, which is capable of removing noise and preserving the continuity in the Kernel. Finally, we developed an adaptive weighted spatial prior to preserve sharp edges in latent image restoration. Extensive experiments testify to the effectiveness of our method on various kinds of challenging examples.
Anton Van Den Hengel - One of the best experts on this subject based on the ideXlab platform.
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ICCV - Self-Paced Kernel Estimation for Robust Blind Image Deblurring
2017 IEEE International Conference on Computer Vision (ICCV), 2017Co-Authors: Dong Gong, Yanning Zhang, Anton Van Den HengelAbstract:The challenge in blind image deblurring is to remove the effects of blur with limited prior information about the nature of the blur process. Existing methods often assume that the blur image is produced by linear convolution with additive Gaussian noise. However, including even a small number of outliers to this model in the Kernel Estimation process can significantly reduce the resulting image quality. Previous methods mainly rely on some simple but unreliable heuristics to identify outliers for Kernel Estimation. Rather than attempt to identify outliers to the model a priori, we instead propose to sequentially identify inliers, and gradually incorporate them into the Estimation process. The selfpaced Kernel Estimation scheme we propose represents a generalization of existing self-paced learning approaches, in which we gradually detect and include reliable inlier pixel sets in a blurred image for Kernel Estimation. Moreover, we automatically activate a subset of significant gradients w.r.t. the reliable inlier pixels, and then update the intermediate sharp image and the Kernel accordingly. Experiments on both synthetic data and real-world images with various kinds of outliers demonstrate the effectiveness and robustness of the proposed method compared to the stateof- the-art methods.
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Self-Paced Kernel Estimation for Robust Blind Image Deblurring
2017 IEEE International Conference on Computer Vision (ICCV), 2017Co-Authors: Dong Gong, Yanning Zhang, Anton Van Den HengelAbstract:The challenge in blind image deblurring is to remove the effects of blur with limited prior information about the nature of the blur process. Existing methods often assume that the blur image is produced by linear convolution with additive Gaussian noise. However, including even a small number of outliers to this model in the Kernel Estimation process can significantly reduce the resulting image quality. Previous methods mainly rely on some simple but unreliable heuristics to identify outliers for Kernel Estimation. Rather than attempt to identify outliers to the model a priori, we instead propose to sequentially identify inliers, and gradually incorporate them into the Estimation process. The self-paced Kernel Estimation scheme we propose represents a generalization of existing self-paced learning approaches, in which we gradually detect and include reliable inlier pixel sets in a blurred image for Kernel Estimation. Moreover, we automatically activate a subset of significant gradients w.r.t. the reliable inlier pixels, and then update the intermediate sharp image and the Kernel accordingly. Experiments on both synthetic data and real-world images with various kinds of outliers demonstrate the effectiveness and robustness of the proposed method compared to the stateof- the-art methods.
Haibo Li - One of the best experts on this subject based on the ideXlab platform.
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Regularized motion blur-Kernel Estimation with adaptive sparse image prior learning
Pattern Recognition, 2016Co-Authors: Wenze Shao, Qi Ge, Haisong Deng, Haibo LiAbstract:This paper proposes a regularized negative log-marginal-likelihood minimization method for motion blur-Kernel Estimation, which is the core problem of blind motion deblurring. In contrast to existing approaches, the proposed method treats the blur-Kernel as a deterministic parameter in a directed graphical model wherein, the sharp image is sparsely modeled by using a three-layer hierarchical Bayesian prior and the inverse noise variance is supposed distributed to the Gamma hyper-prior. By borrowing the ideas of mean filed approximation and iteratively reweighted least squares, the posterior distributions of the sharp image, the inverse noise variance and the hyper-parameters involved in the image prior, as well as the deterministic model parameters including the motion blur-Kernel and those involved in the hyper-priors, are all estimated automatically for each blind motion deblurring problem. It is worthy to note that, the new approach relies on a strict minimization objective function, and learns a more adaptive sparse image prior while with considerably less implementation heuristics compared with existing motion blur-Kernel Estimation approaches. Experimental results on both benchmark and real-world motion blurred images demonstrate that the proposed method has achieved state-of-the-art or even better performance than the current blind motion deblurring approaches in terms of the image deblurring quality. The results also show that the proposed approach is robust to the size of the motion blur-Kernel to a great extent. A new motion blur-Kernel Estimation method is proposed for blind image deblurring.The new method is formulated in a unified and rigorous optimization perspective.Sparse image priors are learned adaptively for each blind deblurring problem.The noise variance is automatically estimated unlike state-of-the art VB methods.The method achieves better performance in terms of deblurring effectiveness
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a unified optimization perspective to single multi observation blur Kernel Estimation with applications to camera shake deblurring and nonparametric blind super resolution
Journal of Mathematical Imaging and Vision, 2016Co-Authors: Wenze Shao, Qi Ge, Haisong Deng, Haibo LiAbstract:The nonparametric blur-Kernel Estimation, using either single image or multi-observation, has been intensively studied since Fergus et al.'s influential work (ACM Trans Graph 25:787---794, 2006). However, in the current literature there is always a gap between the two highly relevant problems; that is, single- and multi-shot blind deconvolutions are modeled and solved independently, lacking a unified optimization perspective. In this paper, we attempt to bridge the gap between the two problems and propose a rigorous and unified minimization function for single/multi-shot blur-Kernel Estimation by coupling the maximum-a-posteriori (MAP) and variational Bayesian (VB) principles. The new function is depicted using a directed graphical model, where the sharp image and the inverse noise variance associated with each shot are treated as random variables, while each blur-Kernel, in difference from existing VB methods, is just modeled as a deterministic parameter. Utilizing a universal, three-level hierarchical prior on the latent sharp image and a Gamma hyper-prior on each inverse noise variance, single/multi-shot blur-Kernel Estimation is uniformly formulated as an $${\varvec{\fancyscript{l}}}_{{0.5}}$$l0.5-norm-regularized negative log-marginal-likelihood minimization problem. By borrowing ideas of expectation-maximization, majorization-minimization, and mean field approximation, as well as iteratively reweighted least squares, all the unknowns of interest, including the sharp image, the blur-Kernels, the inverse noise variances, as well as other relevant parameters are estimated automatically. Compared with most single/multi-shot blur-Kernel Estimation methods, the proposed approach is not only more flexible in processing multiple observations under distinct imaging scenarios due to its independence of the commutative property of convolution but also more adaptive in sparse image modeling while in the meanwhile with much less implementational heuristics. Finally, the proposed blur-Kernel Estimation method is naturally applied to two low-level vision problems, i.e., camera-shake deblurring and nonparametric blind super-resolution. Experiments on benchmark real-world motion blurred images, simulated multiple-blurred images, as well as both synthetic and realistic low-resolution blurred images are conducted, demonstrating the superiority of the proposed approach to state-of-the-art single/multi-shot camera-shake deblurring and nonparametric blind super-resolution methods.
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A Unified Optimization Perspective to Single/Multi-observation Blur-Kernel Estimation with Applications to Camera-Shake Deblurring and Nonparametric Blind Super-Resolution
Journal of Mathematical Imaging and Vision, 2015Co-Authors: Wenze Shao, Qi Ge, Haisong Deng, Haibo LiAbstract:The nonparametric blur-Kernel Estimation, using either single image or multi-observation, has been intensively studied since Fergus et al.'s influential work (ACM Trans Graph 25:787---794, 2006). However, in the current literature there is always a gap between the two highly relevant problems; that is, single- and multi-shot blind deconvolutions are modeled and solved independently, lacking a unified optimization perspective. In this paper, we attempt to bridge the gap between the two problems and propose a rigorous and unified minimization function for single/multi-shot blur-Kernel Estimation by coupling the maximum-a-posteriori (MAP) and variational Bayesian (VB) principles. The new function is depicted using a directed graphical model, where the sharp image and the inverse noise variance associated with each shot are treated as random variables, while each blur-Kernel, in difference from existing VB methods, is just modeled as a deterministic parameter. Utilizing a universal, three-level hierarchical prior on the latent sharp image and a Gamma hyper-prior on each inverse noise variance, single/multi-shot blur-Kernel Estimation is uniformly formulated as an $${\varvec{\fancyscript{l}}}_{{0.5}}$$l0.5-norm-regularized negative log-marginal-likelihood minimization problem. By borrowing ideas of expectation-maximization, majorization-minimization, and mean field approximation, as well as iteratively reweighted least squares, all the unknowns of interest, including the sharp image, the blur-Kernels, the inverse noise variances, as well as other relevant parameters are estimated automatically. Compared with most single/multi-shot blur-Kernel Estimation methods, the proposed approach is not only more flexible in processing multiple observations under distinct imaging scenarios due to its independence of the commutative property of convolution but also more adaptive in sparse image modeling while in the meanwhile with much less implementational heuristics. Finally, the proposed blur-Kernel Estimation method is naturally applied to two low-level vision problems, i.e., camera-shake deblurring and nonparametric blind super-resolution. Experiments on benchmark real-world motion blurred images, simulated multiple-blurred images, as well as both synthetic and realistic low-resolution blurred images are conducted, demonstrating the superiority of the proposed approach to state-of-the-art single/multi-shot camera-shake deblurring and nonparametric blind super-resolution methods.
