CAS: Correlation Adaptive Sparse Modeling for Image Denoising

Abstract

Image restoration techniques generally use intrinsic correlations of image contents to reduce the uncertainty of the unknown signal and estimate the latent ground truth. Local and non-local correlation are the two major kinds of correlations utilized. They are different sources of correlations reflecting connections between different image data, but such a difference is not taken into consideration in most existing schemes. Typically, sparse representation based works use the same image data to exploit both local and non-local correlation in shared regularization. This paper aims to fully exploit local and non-local correlation of image contents separately so that near-optimal sparse representations are achieved and thus the uncertainty of signals is minimized. The proposed scheme adaptively selects different image data to exploit local and non-local correlation respectively. In particular, to exploit local correlation, the image data of interest is extracted from clustered rows of patch groups that consist of similar image contents. Experimental results on image denoising show that the proposed scheme not only outperforms state-of-the-art sparsity and low rank-based methods, but also surpasses recent successful deep learning-based approaches in terms of PSNR, SSIM, and visual quality.