TY - JOUR
T1 - Image Restoration by Iterative Denoising and Backward Projections
AU - Tirer, Tom
AU - Giryes, Raja
N1 - Publisher Copyright:
© 1992-2012 IEEE.
PY - 2019/3
Y1 - 2019/3
N2 - 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.
AB - 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.
KW - Plug-and-play
KW - denoising neural network
KW - image deblurring
KW - image denoising
KW - image inpainting
KW - image restoration
KW - inverse problems
UR - http://www.scopus.com/inward/record.url?scp=85054687906&partnerID=8YFLogxK
U2 - 10.1109/TIP.2018.2875569
DO - 10.1109/TIP.2018.2875569
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AN - SCOPUS:85054687906
SN - 1057-7149
VL - 28
SP - 1220
EP - 1234
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
IS - 3
M1 - 8489894
ER -