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An Iterative Denoising and Backward Projections Method

Citation Author(s):
Tom Tirer, Raja Giryes
Submitted by:
Tom Tirer
Last updated:
5 October 2018 - 5:13pm
Document Type:
Presentation Slides
Document Year:
2018
Event:
Presenters:
Tom Tirer
Paper Code:
WQ.L3.3
 

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 (P&P) 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 work, 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 plug-and-play 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 P&P approach for image inpainting and deblurring. We also demonstrate the advantages of our method for blind-deblurring, where only an inexact estimate of the blur kernel is available, and per-scenario tuning is impossible.

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