DeepRED: Deep Image Prior Powered by RED

TL;DR

This paper enhances the Deep Image Prior method by integrating the RED framework, resulting in improved unsupervised image recovery without requiring denoiser differentiation.

Contribution

It introduces a novel combination of DIP and RED, boosting image reconstruction quality in inverse problems through explicit prior incorporation.

Findings

Improved image recovery results over traditional DIP.

Effective unsupervised reconstruction without denoiser differentiation.

Demonstrated success across multiple inverse imaging problems.

Abstract

Inverse problems in imaging are extensively studied, with a variety of strategies, tools, and theory that have been accumulated over the years. Recently, this field has been immensely influenced by the emergence of deep-learning techniques. One such contribution, which is the focus of this paper, is the Deep Image Prior (DIP) work by Ulyanov, Vedaldi, and Lempitsky (2018). DIP offers a new approach towards the regularization of inverse problems, obtained by forcing the recovered image to be synthesized from a given deep architecture. While DIP has been shown to be quite an effective unsupervised approach, its results still fall short when compared to state-of-the-art alternatives. In this work, we aim to boost DIP by adding an explicit prior, which enriches the overall regularization effect in order to lead to better-recovered images. More specifically, we propose to bring-in the concept of Regularization by Denoising (RED), which leverages existing denoisers for regularizing inverse problems. Our work shows how the two (DIP and RED) can be merged into a highly effective unsupervised recovery process while avoiding the need to differentiate the chosen denoiser, and leading to very effective results, demonstrated for several tested problems.