Non-uniform Single Image Deblurring Based on Sparse Representation and Adaptive Dictionary Learning

Considering the sparseness property of images, a sparse representation based iterative deblurring method is presented for single image deblurring under uniform and non-uniform motion blur. The approach taken is based on sparse and redundant representations over adaptively training dictionaries from single blurred-noisy image itself. Further, the K-SVD algorithm is used to obtain a dictionary that describes the image contents effectively. Comprehensive experimental evaluation demonstrate that the proposed framework integrating the sparseness property of images, adaptive dictionary training and iterative deblurring scheme together significantly improves the deblurring performance and is comparable with the state-of-the art deblurring algorithms and seeks a powerful solution to an ill-conditioned inverse problem.