Image Restoration by Iterative Denoising and Backward Projections
This work addresses the need for more automated and efficient image restoration methods for applications in computer vision, though it is incremental as it builds on the Plug-and-Play framework.
The authors tackled the problem of solving general inverse problems like image deblurring and inpainting with less parameter tuning than existing Plug-and-Play methods, achieving competitive results with task-specific techniques and the P&P approach in empirical tests.
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.