提出可控轨迹的流式展开框架，提升光谱压缩成像重建质量与稳定性。
Progressive Flow-inspired Unfolding for Spectral Compressive Imaging
- 基于扩散轨迹思想设计平滑优化路径，控制重建过程渐进演进。
- 在仿真与真实数据上均优于现有方法，实现更高重建精度与效率。
- 适合需要稳定高质量重建的光谱成像研究者使用。
编码孔径快照光谱成像（CASSI）从单张二维压缩测量中恢复三维高光谱图像（HSI），是一项极具挑战性的重建任务。近年来，基于显式数据保真更新与隐式深度去噪器的深度展开网络（DUNs）已在该任务上达到领先水平。然而，现有展开方法存在重建轨迹不可控问题，导致各阶段质量突变、缺乏渐进优化。受扩散轨迹与流匹配启发，本文提出一种新型可控制轨迹的展开框架，强制从噪声初值到高质量重构之间保持平滑连续的优化路径。为提升计算效率，设计了一种面向高光谱重建的高效时空变换器，并引入频域融合模块以保证特征一致性。在仿真与真实数据上的实验表明，本方法在重建质量和效率方面均优于现有最先进方法。
原文摘要 · Abstract (English)
Coded aperture snapshot spectral imaging (CASSI) retrieves a 3D hyperspectral image (HSI) from a single 2D compressed measurement, which is a highly challenging reconstruction task. Recent deep unfolding networks (DUNs), empowered by explicit data-fidelity updates and implicit deep denoisers, have achieved the state of the art in CASSI reconstruction. However, existing unfolding approaches suffer from uncontrollable reconstruction trajectories, leading to abrupt quality jumps and non-gradual refinement across stages. Inspired by diffusion trajectories and flow matching, we propose a novel trajectory-controllable unfolding framework that enforces smooth, continuous optimization paths from noisy initial estimates to high-quality reconstructions. To achieve computational efficiency, we design an efficient spatial-spectral Transformer tailored for hyperspectral reconstruction, along with a frequency-domain fusion module to gurantee feature consistency. Experiments on simulation and real data demonstrate that our method achieves better reconstruction quality and efficiency than prior state-of-the-art approaches.
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