Color and direction-invariant nonlocal self-similarity prior and its application to color image denoising
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. RESEARCH PAPER .
December 2020, Vol. 63 222101:1–222101:17 https://doi.org/10.1007/s11432-020-2880-3
Color and direction-invariant nonlocal self-similarity prior and its application to color image denoising Qi XIE1 , Qian ZHAO1 , Zongben XU1 & Deyu MENG1,2* 1 School of Mathematics and Statistics, Xi’an Jiaotong University, Shaanxi 710049, China; Macau Institute of Systems Engineering, Macau University of Science and Technology, Macau 999078, China
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Received 4 January 2020/Revised 9 April 2020/Accepted 14 April 2020/Published online 2 November 2020
Abstract Nonlocal self-similarity (NSS) is one of the most commonly used priors in computer vision and image processing. It aims to make use of the fact that a natural image often possesses many repetitive local patterns, and thus a local image patch always has many similar patches across the image. Through compensatively integrating these similar image patches, their insightful patterns hiding under corrupted noises can be intrinsically extracted. However, for using this prior knowledge, current methods search the similar patches by using simple block matching strategy with Euclidean distance, which largely ignores those patches containing similar local patterns but with different texture-directions and colors. To more sufficiently explore similar patches over an image, in this paper, we propose two new representations for image patches, which facilitate an easy NSS prior for measuring direction-invariant and color-invariant nonlocal selfsimilarity possessed by image patches. Specifically, based on this prior term, we formulate the color image denoising problem as a concise Bayesian posterior estimation framework, and design an efficient expectationmaximization (EM) algorithm to solve it. A series of experiments implemented on simulated and real noisy color images demonstrate the superiority of the proposed method as compared with the state-of-the-arts both visually and quantitatively, verifying the potential usefulness of this new NSS prior. Keywords color image denoising, nonlocal self-similarity, Gaussian mixture model, maximum a posterior (MAP) model, EM algorithm Citation Xie Q, Zhao Q, Xu Z B, et al. Color and direction-invariant nonlocal self-similarity prior and its application to color image denoising. Sci China Inf Sci, 2020, 63(12): 222101, https://doi.org/10.1007/s11432-020-2880-3
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Introduction
In most conventional methods designed for various image processing tasks, the key point we need to consider is to finely explore and encode the general prior structure knowledge underlying images. One of the most commonly utilized priors in current research is the so called nonlocal self-similarity (NSS) prior [1], referring to the fact that a natural image (both gray-scale and color ones) often has many repetitive local patterns, and thus a local image patch always has some similar patches across the image. Through compensatively integrating these similar image patches, the negative effects brought by noises can be effectively suppressed and their insightf
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