Scene-adapted plug-and-play algorithm with convergence guarantees
This addresses the challenge of ensuring convergence in flexible image processing frameworks for researchers and practitioners, though it is incremental as it builds on existing plug-and-play and scene-adapted prior concepts.
The paper tackled the convergence analysis of plug-and-play algorithms in image processing by integrating a Gaussian mixture model denoiser into an alternating direction method of multipliers, proving guaranteed convergence and applying it to hyperspectral sharpening.
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.