A Correlation-Based Dissimilarity Measure for Noisy Patches

In this work, we address the problem of defining a robust patch dissimilarity measure for an image corrupted by an additive white Gaussi an noise. The whiteness of the noise, despite being a common assumption that is realistic for RAW images, is hardly used to its full potential by classical denoising methods. In particular, the \(L^2\)-norm is very widely used to evaluate distances and similarities between images or patches. However, we claim that a better dissimilarity measure can be defined to convey more structural information. We propose to compute the dissimilarity between patches by using the autocorrelation of their difference. In order to illustrate the usefulness of this measure, we perform three experiments. First, this new criterion is used in a similar patch detection task. Then, we use it on the Non Local Means (NLM) denoising method and show that it improves performances by a large margin. Finally, it is applied to the task of no-reference evaluation of denoising results, where it shows interesting visual properties. In all those applications, the autocorrelation improves over the \(L^2\)-norm.