Stochastic Image Denoising by Sampling from the Posterior Distribution

@article{Kawar2021StochasticID,
  title={Stochastic Image Denoising by Sampling from the Posterior Distribution},
  author={Bahjat Kawar and Gregory Vaksman and Michael Elad},
  journal={2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)},
  year={2021},
  pages={1866-1875}
}
Image denoising is a well-known and well studied problem, commonly targeting a minimization of the mean squared error (MSE) between the outcome and the original image. Unfortunately, especially for severe noise levels, such Minimum MSE (MMSE) solutions may lead to blurry output images. In this work we propose a novel stochastic denoising approach that produces viable and high perceptual quality results, while maintaining a small MSE. Our method employs Langevin dynamics that relies on a… 

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