FiRe: Fixed-points of Restoration Priors for Solving Inverse Problems

TL;DR

FiRe introduces a novel framework that leverages fixed points of restoration models as priors for inverse problems, expanding the use of pretrained restoration networks within Plug-and-Play algorithms.

Contribution

The paper proposes FiRe, a new fixed-point based prior framework that generalizes traditional denoising priors to broader restoration models in inverse problem solving.

Findings

Effective across various inverse problems

Enables ensemble of multiple restoration models

Unifies acquisition-informed restoration within a single framework

Abstract

Selecting an appropriate prior to compensate for information loss due to the measurement operator is a fundamental challenge in imaging inverse problems. Implicit priors based on denoising neural networks have become central to widely-used frameworks such as Plug-and-Play (PnP) algorithms. In this work, we introduce Fixed-points of Restoration (FiRe) priors as a new framework for expanding the notion of priors in PnP to general restoration models beyond traditional denoising models. The key insight behind FiRe is that smooth images emerge as fixed points of the composition of a degradation operator with the corresponding restoration model. This enables us to derive an explicit formula for our implicit prior by quantifying invariance of images under this composite operation. Adopting this fixed-point perspective, we show how various restoration networks can effectively serve as priors for solving inverse problems. The FiRe framework further enables ensemble-like combinations of multiple restoration models as well as acquisition-informed restoration networks, all within a unified optimization approach. Experimental results validate the effectiveness of FiRe across various inverse problems, establishing a new paradigm for incorporating pretrained restoration models into PnP-like algorithms. Code available at https://github.com/matthieutrs/fire.