Edge Based Blind Single Image Deblurring With Sparse Priors

Blind image deblurring is the estimation of the blur kernel and the latent sharp image from a blurry image. This makes it a significant ill-posed problem with various investigations looking for adequate solutions. The recourse to image priors have been noticed in recent approaches to improve final results. One of the most interesting results are based on data priors. This has been the starting point to the proposed blind image deblurring system. In particular, this study explores the potential of the sparse representation widely known for its efficiency in several reconstruction tasks. In fact, we propose a sparse representation based iterative deblurring method that exploits sparse constraints of edge based image patches. This process includes the K-SVD algorithm useful for the dictionary definition. Our main contributions are (1) the application of a shock filter as a pre-processing step followed by filter sub-bands applications for an effective contour detection, (2) the use of an online training data-sets with elementary patterns to describe edge-based information and (3) the recourse to an adaptative dictionary training. The experimental study illustrates promising results of the proposed deblurring method compared to the well-known state-of-the-art methods.