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arXiv · 2510.01912

Flow-Matching Guided Deep Unfolding for Hyperspectral Image Reconstruction

Yi Ai·Yuanhao Cai·Yulun Zhang·Xin Yuan·Xiaokang Yang
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Abstract

Reconstructing a three-dimensional hyperspectral cube from a two-dimensional compressed measurement is a severely ill-posed inverse problem. Existing deep unfolding networks (DUNs) retain fidelity to the imaging model, but regression-trained denoisers can suppress spatial detail and smooth spectral structure under strong modulation. This paper proposes \emph{FMU}, a deep unfolding framework that couples a measurement-conditioned flow-matching prior with a sensing-model-guided measurement update. A two-phase scheme first learns a compact clean-HSI latent target and then trains a conditional velocity field to generate this target from Gaussian noise. A mean-velocity regularizer additionally penalizes the first-moment error of the learned field. On the KAIST 10-scene benchmark under the optical-filter setting, FMU obtains 42.13\,dB PSNR and 0.9900 SSIM, outperforming LADE-DUN by 1.16\,dB in PSNR under the same training data, sensing mask, and evaluation protocol. Under the same optical-filter operator, FMU is further evaluated on held-out KAIST and ICVL scenes without fine-tuning. We also report quantitative simulated-CASSI results and qualitative reconstructions of real CASSI measurements.

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Yi Ai, Yuanhao Cai, Yulun Zhang, Xin Yuan, Xiaokang Yang. 2025-10-02. Flow-Matching Guided Deep Unfolding for Hyperspectral Image Reconstruction. https://arxiv.org/abs/2510.01912

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