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The recently proposed plug-and-play (PnP) framework allows leveraging recentdevelopments in image denoising to tackle other, more involved, imaging inverseproblems. In a PnP method, a black-box denoiser is plugged into an iterativealgorithm, taking the place of a formal denoising step that corresponds to theproximity operator of some convex regularizer. While this approach offersflexibility and excellent performance, convergence of the resulting algorithmmay be hard to analyze, as most state-of-the-art denoisers lack an explicitunderlying objective function. In this paper, we propose a PnP approach where ascene-adapted prior (i.e., where the denoiser is targeted to the specific scenebeing imaged) is plugged into ADMM (alternating direction method ofmultipliers), and prove convergence of the resulting algorithm. Finally, weapply the proposed framework in two different imaging inverse problems:hyperspectral sharpening/fusion and image deblurring from blurred/noisy imagepairs.

Scene-Adapted Plug-and-Play Algorithm with Guaranteed Convergence: Applications to Data Fusion in Imaging