Regularization by Denoising: Clarifications and New Interpretations

@article{Reehorst2019RegularizationBD,
  title={Regularization by Denoising: Clarifications and New Interpretations},
  author={Edward T. Reehorst and Philip Schniter},
  journal={IEEE Transactions on Computational Imaging},
  year={2019},
  volume={5},
  pages={52-67}
}
Regularization by denoising (RED), as recently proposed by Romano, Elad, and Milanfar, is powerful image-recovery framework that aims to minimize an explicit regularization objective constructed from a plug-in image-denoising function. [] Key Method To explain the RED algorithms, we propose a new framework called Score-Matching by Denoising (SMD), which aims to match a “score” (i.e., the gradient of a log-prior).

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