Physics-Guided Conditional Diffusion Networks for Microwave Image Reconstruction

TL;DR

This paper introduces a physics-guided conditional diffusion network for microwave image reconstruction that generates multiple plausible permittivity maps, improving robustness and fidelity over traditional deterministic methods.

Contribution

The paper presents a novel generative diffusion model integrated with physics-based evaluation for microwave imaging, capturing non-uniqueness and producing multiple reconstructions.

Findings

Generated high-quality permittivity maps with improved shape recognition.

Achieved better generalization using a synthetic scattering features dataset.

Demonstrated robustness and accuracy on synthetic and experimental data.

Abstract

A conditional latent-diffusion based framework for solving the electromagnetic inverse scattering problem associated with microwave imaging is introduced. This generative machine-learning model explicitly mirrors the non-uniqueness of the ill-posed inverse problem. Unlike existing inverse solvers utilizing deterministic machine learning techniques that produce a single reconstruction, the proposed latent-diffusion model generates multiple plausible permittivity maps conditioned on measured scattered-field data, thereby generating several potential instances in the range-space of the non-unique inverse mapping. A forward electromagnetic solver is integrated into the reconstruction pipeline as a physics-based evaluation mechanism. The space of candidate reconstructions form a distribution of possibilities consistent with the conditioning data and the member of this space yielding the lowest scattered-field data discrepancy between the predicted and measured scattered fields is reported as the final solution. Synthetic and experimental labeled datasets are used for training and evaluation of the model. An innovative labeled synthetic dataset is created that exemplifies a varied set of scattering features. Training of the model using this new dataset produces high quality permittivity reconstructions achieving improved generalization with excellent fidelity to shape recognition. The results highlight the potential of hybrid generative physics frameworks as a promising direction for robust, data-driven microwave imaging.