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 self-similarity 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 expectation-maximization (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.

非本地自相似度（NSS）是计算机视觉和图像处理中最常用的先验之一。它旨在利用以下事实：自然图像通常具有许多重复的局部图案，因此局部图像补丁在整个图像上始终具有许多相似的补丁。通过补偿性地集成这些相似的图像块，可以从本质上提取隐藏在损坏的噪声下的有洞察力的模式。然而，对于使用该现有知识，当前方法通过使用具有欧几里得距离的简单块匹配策略来搜索相似补丁，该策略很大程度上忽略了包含相似局部图案但纹理方向和颜色不同的补丁。为了更充分地探索图像上的相似补丁，在本文中，我们提出了两种新的图像补丁表示形式，在测量图像斑块所具有的方向不变和颜色不变的非局部自相似性之前，这些方法有助于简化NSS。具体而言，基于该先前术语，我们将彩色图像降噪问题公式化为简洁的贝叶斯后验估计框架，并设计了一种有效的期望最大化（EM）算法来解决该问题。在模拟和真实噪点彩色图像上进行的一系列实验证明，与视觉和定量方面的最新技术相比，该方法具有优越性，从而验证了该新NSS的潜在用途。我们将彩色图像去噪问题公式化为简洁的贝叶斯后验估计框架，并设计了一种有效的期望最大化（EM）算法来解决该问题。在模拟和真实噪点彩色图像上进行的一系列实验证明，与视觉和定量方面的最新技术相比，该方法具有优越性，从而验证了该新NSS的潜在用途。我们将彩色图像去噪问题公式化为简洁的贝叶斯后验估计框架，并设计了一种有效的期望最大化（EM）算法来解决该问题。在模拟和真实噪点彩色图像上进行的一系列实验证明，与视觉和定量方面的最新技术相比，该方法具有优越性，从而验证了该新NSS的潜在用途。