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 illposed inverse problem and alleviate the undesirable ring effect which is usually suffered by conventional deblurring methods. Experimental results compared with state-of-theart blind deblurring method demonstrate the effectiveness of the proposed method.
Zhang et al. REF used a sparse coding method for sharp image recovering, which assumed that the natural image patch can be sparsely represented by an over-complete dictionary.
SPARSE REPRESENTATION BASED BLIND IMAGE DEBLURRING
2011 IEEE International Conference on Multimedia and Expo
2011 IEEE International Conference on Multimedia and Expo
Computer Science