What is known about the convergence of plug-and-play algorithms?
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Plug-and-play algorithms combine proximal algorithms with denoiser priors to solve inverse problems. These algorithms rely on the computability of the proximal operator of the data fidelity term. Several papers have proposed plug-and-play frameworks based on linearized ADMM that allow bypassing the computation of intractable proximal operators. Convergence of these algorithms has been demonstrated, and results have been provided for restoration tasks such as super-resolution and deblurring with non-uniform blur . Additionally, a new type of plug-and-play method based on half-quadratic splitting has been proposed, where the denoiser is realized as a gradient descent step on a deep neural network. Convergence results for proximal gradient descent algorithms in the non-convex setting have been exploited to show that this algorithm is a convergent iterative scheme targeting stationary points of an explicit global functional. Experimental results confirm the convergence and state-of-the-art performance for various ill-posed inverse problems .
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| Papers (5) | Insight |
|---|---|
2 Citations | The paper discusses the convergence of plug-and-play algorithms, specifically focusing on the convergence of the PnP-ISTA algorithm with adaptive-kernel denoisers. |
| The paper proposes a new type of Plug-and-Play algorithm based on half-quadratic splitting, which is shown to be a convergent iterative scheme targeting stationary points of an explicit global functional. | |
19 Oct 2022 | The paper demonstrates the convergence of a Plug & Play framework based on linearized ADMM for solving inverse problems. |
19 Oct 2022 | The paper demonstrates the convergence of a Plug&Play framework based on linearized ADMM for solving inverse problems such as super-resolution and deblurring with non-uniform blur. |
04 Jun 2023 | The paper proposes a Plug & Play framework based on linearized ADMM and demonstrates the convergence of the algorithm. |
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