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ScatDiff: Physical Diffusion Model for Electromagnetic Computational Imaging
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DOI：10.1109/TGRS.2025.3599591.png)
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Abstract
En 中文
Electromagnetic computational imaging (ECI) offers a promising solution to electromagnetic inverse scattering problems (ISPs). Whereas it is challenged by its ill-posed nature and nonlinearity. Traditional iterative methods are often slow and prone to local minima, while recent deep generative models overlook the physical principles that govern the transformation from scattering fields to images of constitute parameters, limiting their interpretability, generalization, and robustness. To address these issues, we propose ScatDiff, a novel scatter-to-image diffusion model that integrates electromagnetic data with fundamental physical principles. ScatDiff uses a time-aware, backpropagation (BP)-enhanced diffusion to generate noisy images embedded with electromagnetic priors, along with a denoising module that uses cross-attention to adaptively integrate scattering fields. In addition, a physics-driven reconstruction module incorporates an induced current model into the loss function to enhance interpretability. Experiments on three MNIST variants, Gesture dataset, the “Austria” profile, and the “FoamDielExt” profile, show that ScatDiff outperforms traditional iterative methods and deep learning models in both imaging quality and efficiency, with strong generalization and robustness under high noise conditions. Code and datasets are available on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Scatdif</uri>
Keywords:
Computational imaging
diffusion models
image reconstruction
inverse problems
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