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Title: Block Coordinate Regularization by Denoising
We consider the problem of estimating a vector from its noisy measurements using a prior specified only through a denoising function. Recent work on plug- and-play priors (PnP) and regularization-by-denoising (RED) has shown the state- of-the-art performance of estimators under such priors in a range of imaging tasks. In this work, we develop a new block coordinate RED algorithm that decomposes a large-scale estimation problem into a sequence of updates over a small subset of the unknown variables. We theoretically analyze the convergence of the algorithm and discuss its relationship to the traditional proximal optimization. Our analysis complements and extends recent theoretical results for RED-based estimation methods. We numerically validate our method using several denoiser priors, including those based on convolutional neural network (CNN) denoisers.  more » « less
Award ID(s):
1813910
NSF-PAR ID:
10164774
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Advances in neural information processing systems
ISSN:
1049-5258
Page Range / eLocation ID:
382-392
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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