A Patch-Based Composite Denoising Algorithm for Wireless Transmission

Abstract

Many image denoising approaches in recent years are based on patch processing. Particularly, patch-based denoising algorithms under the low-rank (LR) model show outstanding performance in wireless transmission. However, constructing proper dictionaries is a key issue that affects the denoising results. On the basis of this idea, we propose a patch-based composite method for image denoising named similar patch LR (SPLR), which introduces a nonlocal model and structure similarity index measure (SSIM) to evaluate patches similarity. This evaluation method can explore the similarity of image patches to the full extent and construct a similar patches dictionary with an LR property. In addition, to improve the robustness of denoising, we divide the noise part into two parts, and use <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{0}$</tex-math></inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{2,0}$</tex-math></inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-$</tex-math></inline-formula> norm, two different norm constraints to manage different types of noise. Extensive experiments show that the denoising performance and the robustness to noise of SPLR is better than that of the widely used and state-of-the-art, LR image denoising algorithms.