Image denoising based on global image similar patches searching and HOSVD to patches tensor

Abstract

Nonlocal self-similarity of natural image is an essential property for image processing. But how to measure the similarity between different patches and how to better exploit the similarity of patches are two crucial problems for image denoising. In this article, we establish a novel image denoising method based on a global image similar patches searching method and the similar patches tensor high-order singular value decomposition theory. Particularly, in order to find the reasonable similar patches to a reference, a variant of Gaussian mixture model (GMM) global similar patches searching method is proposed, and the graph process unit-based GMM model training method is performed to speed up the training process. Furthermore, the k-means and the local inter-class searching are used to improve on the similarity measure between patches. To better exploit the similarity of patches to image denoising, we rearrange similar patches to a tensor for each reference, and an iterative adaptive weighted tensor low-rank approximation method is established to perform image denoising. Experimental results clearly show that the proposed method is comparable to many recent denoising algorithms from the PNSR, SSIM and NCC viewpoint. For higher noise level (e.g., sigma=50), the performance of our proposed algorithm evinces a 0.06-unit, 0.05-unit and 0.12-unit improvement in the PNSR, SSIM and NCC score, respectively, when compared to state-of-the-art.