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Blind image deblurring using non-negative sparse approximation

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 blurring kernel. With sparsity constraint, an estimate of the sharp image is obtained without solving the ill-posed deconvolution model. Although similar formulation has been proposed but unlike other BID methods the proposed approach is parameter free and requires no prior statistics. The experimental results validate comparatively better performance of proposed method against the other methods.