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Method noise, i.e., the difference between the noisy image and its denoised version, often contains residual image information due to imperfect denoising methods. In this letter, a novel weight is derived for nonlocal means (NLM) denoising, which tries to exploit the nonlocal similarities of residual image in method noise, and estimates directly the similarities between noise free patches instead of the commonly used similarity measure based on noisy observations or their pre-denoised versions. The denoising scheme is implemented in two stages: the first stage is the original NLM, and the second stage is the NLM with the new weight that incorporates the role of both the pre-denoised result and the method noise produced in the previous stage. Experimental results demonstrate the robustness of the proposed weight and its potential with respect to classical NLM methods and other state-of-the-art methods.