# Scene-adapted plug-and-play algorithm with convergence guarantees

## TL;DR

This paper introduces a scene-adapted plug-and-play algorithm using a Gaussian mixture model denoiser within an ADMM framework, providing convergence guarantees and applying it to hyperspectral sharpening.

## Contribution

It develops a convergent plug-and-play method with scene-specific priors, advancing the application of denoising models in image processing tasks.

## Key findings

- Proves convergence of the proposed algorithm.
- Demonstrates effectiveness in hyperspectral sharpening.
- Utilizes scene-adapted priors for improved performance.

## Abstract

Recent frameworks, such as the so-called plug-and-play, allow us to leverage the developments in image denoising to tackle other, and more involved, problems in image processing. As the name suggests, state-of-the-art denoisers are plugged into an iterative algorithm that alternates between a denoising step and the inversion of the observation operator. While these tools offer flexibility, the convergence of the resulting algorithm may be difficult to analyse. In this paper, we plug a state-of-the-art denoiser, based on a Gaussian mixture model, in the iterations of an alternating direction method of multipliers and prove the algorithm is guaranteed to converge. Moreover, we build upon the concept of scene-adapted priors where we learn a model targeted to a specific scene being imaged, and apply the proposed method to address the hyperspectral sharpening problem.