Blind image deblurring using non-negative sparse approximation

Abstract

Blurring is a common source of image degradation in many applications. Blind image deblurring (BID) is an apposite approach for blur removal in real images. Being an ill-posed linear inverse problem, a regularized and well constrained approach is required for a credible solution of BID model. Recently sparse representation base modeling emerged as an efficacious tool in image processing community, with application as regularizer in inverse problems. In this work the sparsity constraint is fused with the non-negative matrix approximation to address the BID problem. An alternative-iterative frame work is developed to estimate the non-negative sparse approximation of the sharp image and blurrin..