Paper Title
Image Restoration In Sparse Domain Using Adaptive Dictionary With Regularization

Sparse representation is one of the powerful statistical image modeling techniques. Of late, sparse modeling techniques have been widely used in the area of image restoration. The basic concept behind the idea is that natural images are sparse in some of the domain. The quality of the recovered image using sparse modeling techniques greatly depends on the sparsity of the domain. In this paper we propose an algorithm which adaptively chooses a sub-dictionary to represent each patch within an image, such that the entire image will have a better sparsified representation. In addition, we introduce an adaptive regularization term called smoothing regularization parameter. The smoothing regularization parameter adds an extra constraint into the l2-minimization problem. The proposed algorithm gives much improved results than most of the state of the art algorithms in terms of PSNR and sharpness. Keywords—Image restoration,sparse modeling, Image deblurring, Regularization.