Improving non-local means image denoising by correlation correction.

Non-local means (NLM) remove noise from an image by a weighted average process. Its distinctive feature is that the weight of a pixel depends on the similarity between two image patches. Patch similarity is measured by the Euclidean distance between two patches and pixels with smaller mutual distance have larger weights. Because of these contextually related weights, image structure can be much better preserved for NLM. Therefore, patch distance is paramount to the denoising performance of NLM. In this paper, we show that a correlation term previously omitted in theoretical analysis cannot be ignored in patch distance calculation, especially when noise is large. We then propose to add a correlation correction function in the distance calculation to remedy this omission. Experimental results show that both quantitative and qualitative performance of the correlation corrected NLM is significantly improved as compared to the other NLM algorithms under large noise. The correlation corrected NLM can also be competitive with the state-of-the-art block-matching 3D (BM3D) algorithm and its execution time is much shorter than BM3D. [ABSTRACT FROM AUTHOR]