Feature-oriented image enhancement using shock filters
SIAM Journal on Numerical Analysis
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Motion-Based Motion Deblurring
IEEE Transactions on Pattern Analysis and Machine Intelligence
Removing camera shake from a single photograph
ACM SIGGRAPH 2006 Papers
Image Deblurring in the Presence of Impulsive Noise
International Journal of Computer Vision
High-quality motion deblurring from a single image
ACM SIGGRAPH 2008 papers
Progressive inter-scale and intra-scale non-blind image deconvolution
ACM SIGGRAPH 2008 papers
ACM SIGGRAPH Asia 2009 papers
Two-phase kernel estimation for robust motion deblurring
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Blind deconvolution using a normalized sparsity measure
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Efficient marginal likelihood optimization in blind deconvolution
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Motion-aware noise filtering for deblurring of noisy and blurry images
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Handling outliers in non-blind image deconvolution
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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In recent years, many image deblurring algorithms have been proposed, most of which assume the noise in the deblurring process satisfies the Gaussian distribution. However, it is often unavoidable in practice both in non-blind and blind image deblurring, due to the error on the input kernel and the outliers in the blurry image. Without proper handing these outliers, the recovered image estimated by previous methods will suffer severe artifacts. In this paper, we mainly deal with two kinds of non-Gaussian noise in the image deblurring process, inaccurate kernel and compressed blurry image, and find that handling the noise as Laplacian distribution can get more robust result in these cases. Based on this point, the new non-blind and blind image deblurring algorithms are proposed to restore the clear image. To get more robust deblurred result, we also use 8 direction gradients of the image to estimate the blur kernel. The new minimization problem can be efficiently solved by the Iteratively Reweighted Least Squares(IRLS) and the experimental results on both synthesized and real-world images show the efficiency and robustness of our algorithm.