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.