Sparse representation based blind image deblurring

We propose a sparse representation based blind image deblurring method. The proposed method exploits the sparsity property of natural images, by assuming that the patches from the natural images can be sparsely represented by an over-complete dictionary. By incorporating this prior into the deblurring process, we can effectively regularize the ill-posed inverse problem and alleviate the undesirable ring effect which is usually suffered by conventional deblurring methods. Experimental results compared with state-of-the-art blind deblurring method demonstrate the effectiveness of the proposed method.

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

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

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

[4]  Michael Elad,et al.  From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images , 2009, SIAM Rev..

[5]  Richard Szeliski,et al.  A content-aware image prior , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Jean Ponce,et al.  Non-uniform Deblurring for Shaken Images , 2010, International Journal of Computer Vision.

[7]  Bernhard Schölkopf,et al.  Space-Variant Single-Image Blind Deconvolution for Removing Camera Shake , 2010, NIPS.

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

[9]  Jean Ponce,et al.  Non-uniform Deblurring for Shaken Images , 2012, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Michael Elad,et al.  On the Role of Sparse and Redundant Representations in Image Processing , 2010, Proceedings of the IEEE.

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

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

[13]  Michael J. Black,et al.  Fields of Experts: a framework for learning image priors , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[14]  Sundaresh Ram,et al.  Removing Camera Shake from a Single Photograph , 2009 .

[15]  Thomas S. Huang,et al.  Image super-resolution as sparse representation of raw image patches , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Frédo Durand,et al.  Understanding and evaluating blind deconvolution algorithms , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

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