Analysis Plug-and-Play Methods for Imaging Inverse Problems

• URL: http://arxiv.org/abs/2509.15422v1
• Date: Thu, 18 Sep 2025 21:01:44 GMT
• Title: Analysis Plug-and-Play Methods for Imaging Inverse Problems
• Authors: Edward P. Chandler, Shirin Shoushtari, Brendt Wohlberg, Ulugbek S. Kamilov,
• Abstract summary: Plug-and-Play Priors is a popular framework for solving inverse imaging problems by integrating priors in the form of denoisers trained to remove noise from images.<n>This paper considers an alternative analysis formulation.<n>in which the prior is imposed on a representation of the image, such as its gradient.
• Score: 10.159516682111322
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Plug-and-Play Priors (PnP) is a popular framework for solving imaging inverse problems by integrating learned priors in the form of denoisers trained to remove Gaussian noise from images. In standard PnP methods, the denoiser is applied directly in the image domain, serving as an implicit prior on natural images. This paper considers an alternative analysis formulation of PnP, in which the prior is imposed on a transformed representation of the image, such as its gradient. Specifically, we train a Gaussian denoiser to operate in the gradient domain, rather than on the image itself. Conceptually, this is an extension of total variation (TV) regularization to learned TV regularization. To incorporate this gradient-domain prior in image reconstruction algorithms, we develop two analysis PnP algorithms based on half-quadratic splitting (APnP-HQS) and the alternating direction method of multipliers (APnP-ADMM). We evaluate our approach on image deblurring and super-resolution, demonstrating that the analysis formulation achieves performance comparable to image-domain PnP algorithms.

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