Data di Pubblicazione:
2024
Citazione:
Constrained Plug-and-Play Priors for Image Restoration / A. Benfenati, P. Cascarano. - In: JOURNAL OF IMAGING. - ISSN 2313-433X. - 10:2(2024 Feb 19), pp. 50.1-50.15. [10.3390/jimaging10020050]
Abstract:
The Plug-and-Play framework has demonstrated that a denoiser can implicitly serve as the
image prior for model-based methods for solving various inverse problems such as image restoration
tasks. This characteristic enables the integration of the flexibility of model-based methods with the
effectiveness of learning-based denoisers. However, the regularization strength induced by denoisers
in the traditional Plug-and-Play framework lacks a physical interpretation, necessitating demanding
parameter tuning. This paper addresses this issue by introducing the Constrained Plug-and-Play
(CPnP) method, which reformulates the traditional PnP as a constrained optimization problem.
In this formulation, the regularization parameter directly corresponds to the amount of noise in
the measurements. The solution to the constrained problem is obtained through the design of an
efficient method based on the Alternating Direction Method of Multipliers (ADMM). Our experiments
demonstrate that CPnP outperforms competing methods in terms of stability and robustness while
also achieving competitive performance for image quality.
Tipologia IRIS:
01 - Articolo su periodico
Keywords:
plug-and-play priors; constrained formulation; image restoration; inverse problems; regularization by denoising; discrepancy principle
Elenco autori:
A. Benfenati, P. Cascarano
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