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Title: Monotonically Convergent Regularization by Denoising
Regularization by denoising (RED) is a widely-used framework for solving inverse problems by leveraging image de-noisers as image priors. Recent work has reported the state-of-the-art performance of RED in a number of imaging applications using pre-trained deep neural nets as denoisers. Despite the recent progress, the stable convergence of RED algorithms remains an open problem. The existing RED theory only guarantees stability for convex data-fidelity terms and nonexpansive denoisers. This work addresses this issue by developing a new monotone RED (MRED) algorithm, whose convergence does not require nonexpansiveness of the deep denoising prior. Simulations on image deblurring and compressive sensing recovery from random matrices show the stability of MRED even when the traditional RED diverges.  more » « less
Award ID(s):
2043134
NSF-PAR ID:
10416068
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
IEEE International Conference on Image Processing (ICIP)
Page Range / eLocation ID:
426 to 430
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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