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Inverse problems appear in many applications, such as image deblurring andinpainting. The common approach to address them is to design a specificalgorithm for each problem. The Plug-and-Play (P&P) framework, which has beenrecently introduced, allows solving general inverse problems by leveraging theimpressive capabilities of existing denoising algorithms. While this freshstrategy has found many applications, a burdensome parameter tuning is oftenrequired in order to obtain high-quality results. In this work, we propose analternative method for solving inverse problems using off-the-shelf denoisers,which requires less parameter tuning. First, we transform a typical costfunction, composed of fidelity and prior terms, into a closely related, noveloptimization problem. Then, we propose an efficient minimization scheme with aplug-and-play property, i.e., the prior term is handled solely by a denoisingoperation. Finally, we present an automatic tuning mechanism to set themethod's parameters. We provide a theoretical analysis of the method, andempirically demonstrate its competitiveness with task-specific techniques andthe P&P approach for image inpainting and deblurring.

Image Restoration by Iterative Denoising and Backward Projections