Learned Regularization for Microwave Tomography

• URL: http://arxiv.org/abs/2508.08114v1
• Date: Mon, 11 Aug 2025 15:54:58 GMT
• Title: Learned Regularization for Microwave Tomography
• Authors: Bowen Tong, Hao Chen, Shaorui Guo, Dong Liu,
• Abstract summary: Single-Step Diffusion Regularization (SSD-Reg) is a novel approach that embeds diffusion priors into the iterative reconstruction process.<n>SSD-Reg maintains fidelity to both the governing physics and learned structural details.<n>It provides a flexible and effective solution for tackling the ill-posedness inherent in functional image reconstruction.
• Score: 7.792752191078406
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Microwave Tomography (MWT) aims to reconstruct the dielectric properties of tissues from measured scattered electromagnetic fields. This inverse problem is highly nonlinear and ill-posed, posing significant challenges for conventional optimization-based methods, which, despite being grounded in physical models, often fail to recover fine structural details. Recent deep learning strategies, including end-to-end and post-processing networks, have improved reconstruction quality but typically require large paired training datasets and may struggle to generalize. To overcome these limitations, we propose a physics-informed hybrid framework that integrates diffusion models as learned regularization within a data-consistency-driven variational scheme. Specifically, we introduce Single-Step Diffusion Regularization (SSD-Reg), a novel approach that embeds diffusion priors into the iterative reconstruction process, enabling the recovery of complex anatomical structures without the need for paired data. SSD-Reg maintains fidelity to both the governing physics and learned structural distributions, improving accuracy, stability, and robustness. Extensive experiments demonstrate that SSD-Reg, implemented as a Plug-and-Play (PnP) module, provides a flexible and effective solution for tackling the ill-posedness inherent in functional image reconstruction.

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