Invertible ResNets for Inverse Imaging Problems: Competitive Performance with Provable Regularization Properties

• URL: http://arxiv.org/abs/2409.13482v2
• Date: Mon, 16 Dec 2024 16:04:31 GMT
• Title: Invertible ResNets for Inverse Imaging Problems: Competitive Performance with Provable Regularization Properties
• Authors: Clemens Arndt, Judith Nickel,
• Abstract summary: Recent works by Arndt et al addressed the gap by analyzing a data-driven reconstruction method based on invertible residual networks (iResNets) We extend some of the theoretical results from Arndt et al to encompass nonlinear inverse problems and offer insights for the design of large-scale performant iResNet architectures.
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• Abstract: Learning-based methods have demonstrated remarkable performance in solving inverse problems, particularly in image reconstruction tasks. Despite their success, these approaches often lack theoretical guarantees, which are crucial in sensitive applications such as medical imaging. Recent works by Arndt et al (2023 Inverse Problems 39 125018, 2024 Inverse Problems 40 045021) addressed this gap by analyzing a data-driven reconstruction method based on invertible residual networks (iResNets). They revealed that, under reasonable assumptions, this approach constitutes a convergent regularization scheme. However, the performance of the reconstruction method was only validated on academic toy problems and small-scale iResNet architectures. In this work, we address this gap by evaluating the performance of iResNets on two real-world imaging tasks: a linear blurring operator and a nonlinear diffusion operator. To do so, we extend some of the theoretical results from Arndt et al to encompass nonlinear inverse problems and offer insights for the design of large-scale performant iResNet architectures. Through numerical experiments, we compare the performance of our iResNet models against state-of-the-art neural networks, confirming their efficacy. Additionally, we numerically investigate the theoretical guarantees of this approach and demonstrate how the invertibility of the network enables a deeper analysis of the learned forward operator and its learned regularization.

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