Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue ConditionDownload PDF

21 May 2021, 20:47 (edited 26 Oct 2021)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: inverse problems, compressive sensing, plug-and-play priors, regularization by denoising
  • TL;DR: We provide new recovery bounds for PnP under assumptions used in compressed sensing for generative models.
  • Abstract: The plug-and-play priors (PnP) and regularization by denoising (RED) methods have become widely used for solving inverse problems by leveraging pre-trained deep denoisers as image priors. While the empirical imaging performance and the theoretical convergence properties of these algorithms have been widely investigated, their recovery properties have not previously been theoretically analyzed. We address this gap by showing how to establish theoretical recovery guarantees for PnP/RED by assuming that the solution of these methods lies near the fixed-points of a deep neural network. We also present numerical results comparing the recovery performance of PnP/RED in compressive sensing against that of recent compressive sensing algorithms based on generative models. Our numerical results suggest that PnP with a pre-trained artifact removal network provides significantly better results compared to the existing state-of-the-art methods.
  • Supplementary Material: pdf
  • Code Of Conduct: I certify that all co-authors of this work have read and commit to adhering to the NeurIPS Statement on Ethics, Fairness, Inclusivity, and Code of Conduct.
  • Code: https://github.com/wustl-cig/pnp-recovery
9 Replies

Loading