Image Deblurring Algorithm Based on Dictionary Learning

In order to improve the quality and visual effects of blurred images, we proposes an image deblurring algorithm based on dictionary learning. Firstly, we divides the blurred image into image block structure groups, then we recovers the image block through the K-SVD dictionary and the PCA dictionary, and finally the morphological operations is applied to the difference image to obtain restored image. Experimental results show that the proposed algorithm is better than others in peak structural similarity and visual effect.

[1]  Karen O. Egiazarian,et al.  BM3D Frames and Variational Image Deblurring , 2011, IEEE Transactions on Image Processing.

[2]  Suprava Patnaik,et al.  UNIFORM AND NON -UNIFORM SINGLE IMAGE DEBLURRING BASED ON SPARSE REPRESENTATION AND ADAPTIVE DICTIONARY LEARNING , 2014 .

[3]  Javier Portilla,et al.  Image restoration through l0 analysis-based sparse optimization in tight frames , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[4]  Lei Zhang,et al.  Centralized sparse representation for image restoration , 2011, 2011 International Conference on Computer Vision.

[5]  Edward R. Vrscay,et al.  SSIM-inspired image restoration using sparse representation , 2012, EURASIP Journal on Advances in Signal Processing.

[6]  Wen Gao,et al.  Group-Based Sparse Representation for Image Restoration , 2014, IEEE Transactions on Image Processing.

[7]  Jean-Michel Morel,et al.  A non-local algorithm for image denoising , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[8]  David Zhang,et al.  Two-stage image denoising by principal component analysis with local pixel grouping , 2010, Pattern Recognit..

[9]  José M. Bioucas-Dias,et al.  A New TwIST: Two-Step Iterative Shrinkage/Thresholding Algorithms for Image Restoration , 2007, IEEE Transactions on Image Processing.

[10]  Lei Zhang,et al.  Nonlocally Centralized Sparse Representation for Image Restoration , 2013, IEEE Transactions on Image Processing.

[11]  Michal Irani,et al.  Motion Analysis for Image Enhancement: Resolution, Occlusion, and Transparency , 1993, J. Vis. Commun. Image Represent..

[12]  Stephen M. Smith,et al.  SUSAN—A New Approach to Low Level Image Processing , 1997, International Journal of Computer Vision.