  Inverse problems appear in many applications, such as image deblurring and
inpainting. The common approach to address them is to design a specific
algorithm for each problem. The Plug-and-Play (P&P) framework, which has been
recently introduced, allows solving general inverse problems by leveraging the
impressive capabilities of existing denoising algorithms. While this fresh
strategy has found many applications, a burdensome parameter tuning is often
required in order to obtain high-quality results. In this work, we propose an
alternative method for solving inverse problems using off-the-shelf denoisers,
which requires less parameter tuning. First, we transform a typical cost
function, composed of fidelity and prior terms, into a closely related, novel
optimization problem. Then, we propose an efficient minimization scheme with a
plug-and-play property, i.e., the prior term is handled solely by a denoising
operation. Finally, we present an automatic tuning mechanism to set the
method's parameters. We provide a theoretical analysis of the method, and
empirically demonstrate its competitiveness with task-specific techniques and
the P&P approach for image inpainting and deblurring.
