ABSTRACT

Weighted nuclear norm minimization (WNNM) has produced remarkable denoising results; however, it still has some limitations, including only being able to measure the similarity of noisy patches by Euclidean distance, fixing the feedback proportion of method noise for all noise levels, and establishing an inflexible number of iterations for each image. In this paper, three strategies are used to improve the abovementioned shortcomings. The first strategy is to calculate the correlation coefficient using Grey theory, and it is combined with Euclidean distance as the final similarity measure value. The second strategy is to adaptively add the feedback coefficient of the method noise according to various noise levels. The last strategy is to apply a stopping criterion based on residual noise to the iteration process. Experimental results show our method provides better results compared to various state-of-the-art algorithms.

摘要

加权核规范最小化（WNNM）产生了显着的去噪效果。但是，它仍然存在一些局限性，包括只能通过欧几里得距离来测量噪声斑块的相似度，固定所有噪声水平下方法噪声的反馈比例以及为每个图像建立固定的迭代次数。本文采用三种策略来改善上述缺点。第一种策略是使用格雷理论计算相关系数，并将其与欧几里得距离相结合作为最终的相似性度量值。第二种策略是根据各种噪声级别自适应地添加方法噪声的反馈系数。最后一种策略是将基于残留噪声的停止标准应用于迭代过程。