Image deblurring with low-rank approximation structured sparse representation

In recent years sparse representation model (SRM) based image deblurring approaches have shown promising image deblurring results. However, since most of the current SRMs don´t utilize the spatial correlations between the nonzero sparse coefficients, the SRM-based image deblurring methods often fail to faithfully recover sharp image edges. In this paper, a structured SRM is employed to exploit the local and nonlocal spatial correlation between the sparse codes. The connection between the structured SRM and the low-rank approximation model has also been exploited. An effective image deblurring algorithm using the patch-based structured SRM is then proposed. Experimental results demonstrate the improvements of the proposed deblurring method over current state-of-the-art image deblurring methods.