Flow-Matching Guided Deep Unfolding for Hyperspectral Image Reconstruction
This work addresses the problem of accurate hyperspectral image reconstruction for imaging applications, representing an incremental improvement by combining existing techniques in a novel way.
The paper tackles hyperspectral image reconstruction from compressed measurements by proposing FMU, a method integrating flow matching into a deep unfolding framework, which significantly outperforms existing approaches in reconstruction quality on simulated and real datasets.
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 https://github.com/YiAi03/FMU.