Denoising an Image by Denoising Its Curvature Image

@article{Bertalmo2014DenoisingAI,
  title={Denoising an Image by Denoising Its Curvature Image},
  author={Marcelo Bertalm{\'i}o and Stacey Levine},
  journal={SIAM J. Imaging Sciences},
  year={2014},
  volume={7},
  pages={187-211}
}
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 peak signal-to-noise ratio 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. 

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