Image Restoration by Iterative Denoising and Backward Projections

Tom Tirer*, Raja Giryes

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Inverse problems appear in many applications, such as image deblurring and inpainting. The common approach to address them is to design a specific algorithm for each problem. The Plug-and-Play (PP) framework, which has been recently introduced, allows solving general inverse problems by leveraging the impressive capabilities of existing denoising algorithms. While this fresh strategy has found many applications, a burdensome parameter tuning is often required in order to obtain high-quality results. In this paper, we propose an alternative method for solving inverse problems using off-the-shelf denoisers, which requires less parameter tuning. First, we transform a typical cost function, composed of fidelity and prior terms, into a closely related, novel optimization problem. Then, we propose an efficient minimization scheme with a PP property, i.e., the prior term is handled solely by a denoising operation. Finally, we present an automatic tuning mechanism to set the method's parameters. We provide a theoretical analysis of the method and empirically demonstrate its competitiveness with task-specific techniques and the PP approach for image inpainting and deblurring.

Original languageEnglish
Article number8489894
Pages (from-to)1220-1234
Number of pages15
JournalIEEE Transactions on Image Processing
Issue number3
StatePublished - Mar 2019


  • Plug-and-play
  • denoising neural network
  • image deblurring
  • image denoising
  • image inpainting
  • image restoration
  • inverse problems


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