A Fast Stochastic Plug-and-Play ADMM for Imaging Inverse Problems

Junqi Tang, Mike Davies

Research output: Working paper

Abstract / Description of output

In this work we propose an efficient stochastic plug-and-play (PnP) algorithm for imaging inverse problems. The PnP stochastic gradient descent methods have been recently proposed and shown improved performance in some imaging applications over standard deterministic PnP methods. However, current stochastic PnP methods need to frequently compute the image denoisers which can be computationally expensive. To overcome this limitation, we propose a new stochastic PnP-ADMM method which is based on introducing stochastic gradient descent inner-loops within an inexact ADMM framework. We provide the theoretical guarantee on the fixed-point convergence for our algorithm under standard assumptions. Our numerical results demonstrate the effectiveness of our approach compared with state-of-the-art PnP methods.
Original languageUndefined/Unknown
Number of pages5
Publication statusPublished - 20 Jun 2020

Keywords / Materials (for Non-textual outputs)

  • math.OC
  • cs.CV

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