  In this paper, we propose a novel image denoising algorithm using
collaborative support-agnostic sparse reconstruction. An observed image is
first divided into patches. Similarly structured patches are grouped together
to be utilized for collaborative processing. In the proposed collaborative
schemes, similar patches are assumed to share the same support taps. For sparse
reconstruction, the likelihood of a tap being active in a patch is computed and
refined through a collaboration process with other similar patches in the same
group. This provides very good patch support estimation, hence enhancing the
quality of image restoration. Performance comparisons with state-of-the-art
algorithms, in terms of SSIM and PSNR, demonstrate the superiority of the
proposed algorithm.
