Blind image deblurring via coupled sparse representation

Abstract The problem of blind image deblurring is more challenging than that of non-blind image deblurring, due to the lack of knowledge about the point spread function in the imaging process. In this paper, a learning-based method of estimating blur kernel under the l 0 regularization sparsity constraint is proposed for blind image deblurring. Specifically, we model the patch-based matching between the blurred image and its sharp counterpart via a coupled sparse representation. Once the blur kernel is obtained, a non-blind deblurring algorithm can be applied to the final recovery of the sharp image. Our experimental results show that the visual quality of restored sharp images is competitive with the state-of-the-art algorithms for both synthetic and real images.

[1]  Zhou Wang,et al.  Image Quality Assessment: From Error Measurement to Structural Similarity , 2004 .

[2]  William T. Freeman,et al.  Removing camera shake from a single photograph , 2006, SIGGRAPH 2006.

[3]  Deepa Kundur,et al.  Blind Image Deconvolution , 2001 .

[4]  Michael Elad,et al.  Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal Matching Pursuit , 2008 .

[5]  Rafael Molina,et al.  Blind Image Deconvolution: Problem formulation and existing approaches , 2007 .

[6]  K. Egiazarian,et al.  Blind image deconvolution , 2007 .

[7]  Stefano Soatto,et al.  Direct Sparse Deblurring , 2010, Journal of Mathematical Imaging and Vision.

[8]  Aggelos K. Katsaggelos,et al.  Blind Deconvolution Using a Variational Approach to Parameter, Image, and Blur Estimation , 2006, IEEE Transactions on Image Processing.

[9]  Sunghyun Cho,et al.  Fast motion deblurring , 2009, SIGGRAPH 2009.

[10]  Yanning Zhang,et al.  Sparse representation based iterative incremental image deblurring , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[11]  Nikolas P. Galatsanos,et al.  A variational approach for Bayesian blind image deconvolution , 2004, IEEE Transactions on Signal Processing.

[12]  Dahua Lin,et al.  Coupled space learning of image style transformation , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[13]  Thomas S. Huang,et al.  Sparse representation based blind image deblurring , 2011, 2011 IEEE International Conference on Multimedia and Expo.

[14]  Marc Teboulle,et al.  Fast Gradient-Based Algorithms for Constrained Total Variation Image Denoising and Deblurring Problems , 2009, IEEE Transactions on Image Processing.

[15]  Shree K. Nayar,et al.  Motion-based motion deblurring , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Richard Szeliski,et al.  PSF estimation using sharp edge prediction , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Yehoshua Y. Zeevi,et al.  Quasi Maximum Likelihood Blind Deconvolution of Images Using Optimal Sparse Representations , 2003 .

[18]  Ming-Hsuan Yang,et al.  Single image deblurring with adaptive dictionary learning , 2010, 2010 IEEE International Conference on Image Processing.

[19]  Michael Elad,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.

[20]  Bobby Bodenheimer,et al.  Synthesis and evaluation of linear motion transitions , 2008, TOGS.

[21]  Rob Fergus,et al.  Blind deconvolution using a normalized sparsity measure , 2011, CVPR 2011.

[22]  Thomas S. Huang,et al.  Image Super-Resolution Via Sparse Representation , 2010, IEEE Transactions on Image Processing.

[23]  Michael Elad,et al.  Compression of facial images using the K-SVD algorithm , 2008, J. Vis. Commun. Image Represent..

[24]  I. Daubechies,et al.  Iteratively reweighted least squares minimization for sparse recovery , 2008, 0807.0575.

[25]  Ankit Gupta,et al.  Single Image Deblurring Using Motion Density Functions , 2010, ECCV.

[26]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[27]  Jian-Feng Cai,et al.  Blind motion deblurring from a single image using sparse approximation , 2009, CVPR.

[28]  Rob Fergus,et al.  Fast Image Deconvolution using Hyper-Laplacian Priors , 2009, NIPS.

[29]  Ying Wu,et al.  Motion from blur , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  Michael Elad,et al.  Stable recovery of sparse overcomplete representations in the presence of noise , 2006, IEEE Transactions on Information Theory.

[31]  Jiaya Jia,et al.  High-quality motion deblurring from a single image , 2008, ACM Trans. Graph..

[32]  Xavier Bresson,et al.  Bregmanized Nonlocal Regularization for Deconvolution and Sparse Reconstruction , 2010, SIAM J. Imaging Sci..

[33]  Aggelos K. Katsaggelos,et al.  Variational Bayesian Blind Deconvolution Using a Total Variation Prior , 2009, IEEE Transactions on Image Processing.

[34]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.