Image reconstruction algorithm based on group sparse coefficient estimation

Sparse representation based image prior information model has been widely used in image reconstruction. Aiming at the key problems of dictionary selection and coefficient estimation in sparse representation, this paper proposes the image reconstruction method based on sparse representation combined with nonlocal self-similarity. Firstly, the patch matching based on Euclidean distance is used to search the similar image patches; then, the local and nonlocal sparse representation of the similar image patch set is performed using left and right dictionaries respectively, so that the sparser and more accurate sparse representation coefficients are obtained. Next, aiming at the problem of the insufficient sparse coefficient estimation accuracy of the traditional threshold shrinkage method, this paper adopts Bregman iteration algorithm to solve the reconstruction model fast and efficiently; and the Linear Minimum Mean-square Error (LMMSE) estimation criterion is adopted to achieve the sparse coefficient estimation, which can ensure the accurate estimation of the small coefficients containing the information of the image texture details. The experiment results demonstrate that the proposed method not only achieves the state-of-the-art performance in the objective specifications such as peak signal-to-noise ratio (PSNR) and etc., but also makes the reconstructed image have richer detail information and the overall visual effect clearer. © 2015, Science Press. All right reserved.