Convergent Primal-Dual Plug-and-Play Image Restoration: A General Algorithm and Applications
Yodai Suzuki, Ryosuke Isono, Shunsuke Ono

TL;DR
This paper introduces a convergent deep plug-and-play algorithm for image restoration that integrates primal-dual splitting, providing theoretical guarantees and broad applicability to various restoration tasks.
Contribution
It develops a general PnP framework with convergence guarantees by combining primal-dual splitting, addressing limitations of existing methods in handling complex data-fidelity terms.
Findings
The proposed algorithm guarantees convergence under reasonable conditions.
It effectively handles a broad class of image restoration problems.
Numerical experiments validate the method's practicality and effectiveness.
Abstract
We propose a general deep plug-and-play (PnP) algorithm with a theoretical convergence guarantee. PnP strategies have demonstrated outstanding performance in various image restoration tasks by exploiting the powerful priors underlying Gaussian denoisers. However, existing PnP methods often lack theoretical convergence guarantees under realistic assumptions due to their ad-hoc nature, resulting in inconsistent behavior. Moreover, even when convergence guarantees are provided, they are typically designed for specific settings or require a considerable computational cost in handling non-quadratic data-fidelity terms and additional constraints, which are key components in many image restoration scenarios. To tackle these challenges, we integrate the PnP paradigm with primal-dual splitting (PDS), an efficient proximal splitting methodology for solving a wide range of convex optimization…
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Taxonomy
TopicsPhotoacoustic and Ultrasonic Imaging · Advanced X-ray and CT Imaging · Medical Imaging Techniques and Applications
MethodsPnP
