  Nonlocal image representation has been successfully used in many
image-related inverse problems including denoising, deblurring and deblocking.
However, a majority of reconstruction methods only exploit the nonlocal
self-similarity (NSS) prior of the degraded observation image, it is very
challenging to reconstruct the latent clean image. In this paper we propose a
novel model for image denoising via group sparsity residual and external NSS
prior. To boost the performance of image denoising, the concept of group
sparsity residual is proposed, and thus the problem of image denoising is
transformed into one that reduces the group sparsity residual. Due to the fact
that the groups contain a large amount of NSS information of natural images, we
obtain a good estimation of the group sparse coefficients of the original image
by the external NSS prior based on Gaussian Mixture model (GMM) learning and
the group sparse coefficients of noisy image is used to approximate the
estimation. Experimental results have demonstrated that the proposed method not
only outperforms many state-of-the-art methods, but also delivers the best
qualitative denoising results with finer details and less ringing artifacts.
