512 research outputs found
Scene-adapted plug-and-play algorithm with convergence guarantees
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
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