Abstract
Hyperspectral imaging (HSI) provides rich spatial-spectral information but remains costly to acquire due to hardware limitations and the difficulty of reconstructing three-dimensional data from compressed measurements. Although compressive sensing systems such as CASSI improve efficiency, accurate reconstruction is still challenged by severe degradation and loss of fine spectral details. We propose the Flow-Matching-guided Unfolding network (FMU), which, to our knowledge, is the first to integrate flow matching into HSI reconstruction by embedding its generative prior within a deep unfolding framework. To further strengthen the learned dynamics, we introduce a mean velocity loss that enforces global consistency of the flow, leading to a more robust and accurate reconstruction. This hybrid design leverages the interpretability of optimization-based methods and the generative capacity of flow matching. Extensive experiments on both simulated and real datasets show that FMU significantly outperforms existing approaches in reconstruction quality. Code and models will be available at this https URL.
Abstract (translated)
高光谱成像（HSI）提供了丰富的空间-光谱信息，但由于硬件限制和从压缩测量中重构三维数据的难度，其获取成本仍然较高。尽管诸如CASSI之类的压缩感知系统提高了效率，但准确重建仍受到严重退化和精细光谱细节丢失的挑战。我们提出了Flow-Matching指导下的展层网络（FMU），据我们所知，这是首次将流匹配集成到HSI重构中，在深度解卷积框架内嵌入其生成先验的方法。为了进一步增强学习的动力学，我们引入了平均速度损失，以强制执行流的全局一致性，从而实现更稳健和准确的重建。这种混合设计利用了基于优化方法的可解释性和流匹配的生成能力。在仿真数据集和真实数据集上的广泛实验表明，FMU在重构质量上显著优于现有方法。代码和模型将在以下网址提供：[此链接]（请将方括号内的文本替换为实际提供的URL）。
URL
https://arxiv.org/abs/2510.01912
