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In this article we argue that when an image is corrupted by additive noise, its curvature image is less affected by it, i.e. the PSNR of the curvature image is larger. We speculate that, given a denoising method, we may obtain better results by applying it to the curvature image and then reconstructing from it a clean image, rather than denoising the original image directly. Numerical experiments confirm this for several PDE-based and patch-based denoising algorithms.The first author acknowledges partial support by European Research Council, Starting Grant ref. 306337, and/nby Spanish grants AACC, ref. TIN2011-15954-E, and Plan Nacional, ref. TIN2012-38112. The second author was supported in part by NSF-DMS #0915219

Denoising an Image by Denoising Its Curvature Image