Back-Projection Diffusion: Solving the Wideband Inverse Scattering Problem with Diffusion Models

• URL: http://arxiv.org/abs/2408.02866v2
• Date: Fri, 9 Aug 2024 13:44:38 GMT
• Title: Back-Projection Diffusion: Solving the Wideband Inverse Scattering Problem with Diffusion Models
• Authors: Borong Zhang, Martín Guerra, Qin Li, Leonardo Zepeda-Núñez,
• Abstract summary: We present an end-to-end probabilistic framework for approximating the posterior distribution from wideband scattering data. We use conditional diffusion models coupled with the underlying physics of wave-propagation and symmetries in the problem. Our framework is able to provide sharp reconstructions effortlessly, even recovering sub-Nyquist features in the multiple-scattering regime.
• Score: 2.717354728562311
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present Wideband back-projection diffusion, an end-to-end probabilistic framework for approximating the posterior distribution induced by the inverse scattering map from wideband scattering data. This framework leverages conditional diffusion models coupled with the underlying physics of wave-propagation and symmetries in the problem, to produce highly accurate reconstructions. The framework introduces a factorization of the score function into a physics-based latent representation inspired by the filtered back-propagation formula and a conditional score function conditioned on this latent representation. These two steps are also constrained to obey symmetries in the formulation while being amenable to compression by imposing the rank structure found in the filtered back-projection formula. As a result, empirically, our framework is able to provide sharp reconstructions effortlessly, even recovering sub-Nyquist features in the multiple-scattering regime. It has low-sample and computational complexity, its number of parameters scales sub-linearly with the target resolution, and it has stable training dynamics.

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