Improving the denoising of WNNM-based imagery: three different strategies

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.