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.
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Block Coordinate Regularization by Denoising
We consider the problem of reconstructing an image from its noisy measurements using a prior specified only with an image denoiser. Recent work on plug-and-play priors (PnP) and regularization by denoising (RED) has shown the state-of-the-art performance of image reconstruction algorithms under such priors in a range of imaging problems. 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 denoising priors, including those based on deep neural nets.
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- Award ID(s):
- 1813910
- NSF-PAR ID:
- 10164772
- Date Published:
- Journal Name:
- IEEE Transactions on Computational Imaging
- ISSN:
- 2573-0436
- Page Range / eLocation ID:
- 1 to 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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