Blind image deblurring via gradient orientation-based clustered coupled sparse dictionaries

In this paper, we proposed a novel sparse representation-based blind image deblurring algorithm, which exploits the benefits of coupled sparse dictionary, and patch gradient orientation-based sparsifying sub-dictionary learning. We jointly trained coupled dictionaries for blurred and clear image patches to take advantages of the similarity of sparse representation in the blurred and clear image patch pair with respect to their corresponding dictionaries. The first step of the algorithm is to estimate blur kernel from the test image itself which is utilized in generating blur image training set from the clear image training set. Instead of learning a large coupled dictionary, we have proposed to cluster the patches having similar geometric structures and learn smaller sub-dictionaries for each group to improve the effectiveness of sparse modeling of the information in an image. While reconstructing the image, the sparse representation of a blurred image patch is applied to the blur-free dictionary to generate a blur-free image patch. For choosing a sub-dictionary which best describes a particular patch, minimum residue error criterion is formulated. An iterative error compensation mechanism is carried out to enhance the deblurring performance and to compensate for sparse approximation. The performance of proposed deblurring method is evaluated in terms of PSNR, SSIM, ISNR, and visual quality results. The simulation results demonstrate that the proposed method achieves very competitive deblurring performance as compared to other complementary blind deblurring methods.