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Solving RED With Weighted Proximal Methods
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DOI：10.1109/LSP.2020.2979062.png)
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REgularization by Denoising (RED) is an attractive framework for solving inverse problems by incorporating state-of-the-art denoising algorithms as the priors. A drawback of this approach is the high computational complexity of denoisers, which dominate the computation time. In this paper, we apply a general framework called weighted proximal methods (WPMs) to solve RED efficiently. We first show that two recently introduced RED solvers (using the fixed point and accelerated proximal gradient methods) are particular cases of WPMs. Then we show by numerical experiments that slightly more sophisticated variants of WPM can lead to reduced run times for RED by requiring a significantly smaller number of calls to the denoiser.
Keyword:
Signal processing algorithms
Noise reduction
Inverse problems
Acceleration
Approximation algorithms
Minimization
Task analysis
Inverse problem
denoising algorithms
RED
weighted proximal methods
weighting
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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