Regularization by Denoising via Fixed-Point Projection (RED-PRO)
This work addresses theoretical gaps in widely used image recovery methods, offering a more robust framework for researchers and practitioners in computational imaging.
The paper tackled the incomplete theoretical justification of Plug-and-Play Prior (PnP) and Regularization by Denoising (RED) methods in image inverse problems by reformulating RED as a convex optimization problem called RED-PRO, which provides convergence guarantees and shows improved results in image deblurring and super-resolution tasks.
Inverse problems in image processing are typically cast as optimization tasks, consisting of data-fidelity and stabilizing regularization terms. A recent regularization strategy of great interest utilizes the power of denoising engines. Two such methods are the Plug-and-Play Prior (PnP) and Regularization by Denoising (RED). While both have shown state-of-the-art results in various recovery tasks, their theoretical justification is incomplete. In this paper, we aim to bridge between RED and PnP, enriching the understanding of both frameworks. Towards that end, we reformulate RED as a convex optimization problem utilizing a projection (RED-PRO) onto the fixed-point set of demicontractive denoisers. We offer a simple iterative solution to this problem, by which we show that PnP proximal gradient method is a special case of RED-PRO, while providing guarantees for the convergence of both frameworks to globally optimal solutions. In addition, we present relaxations of RED-PRO that allow for handling denoisers with limited fixed-point sets. Finally, we demonstrate RED-PRO for the tasks of image deblurring and super-resolution, showing improved results with respect to the original RED framework.