标题： 基于渐进流的谱压缩成像展开 显示英文标题

标题： Progressive Flow-inspired Unfolding for Spectral Compressive Imaging

摘要： 编码孔径快照光谱成像（CASSI）从单个二维压缩测量中恢复三维高光谱图像（HSI），这是一个高度具有挑战性的重建任务。 最近的深度展开网络（DUNs），通过显式的数据保真更新和隐式的深度去噪器，已经在CASSI重建中达到了最先进水平。 然而，现有的展开方法存在不可控的重建轨迹，导致质量突然跳跃并在不同阶段之间无法渐进优化。 受扩散轨迹和流匹配的启发，我们提出了一种新型的轨迹可控展开框架，该框架从噪声初始估计到高质量重建强制执行平滑、连续的优化路径。 为了实现计算效率，我们设计了一个针对高光谱重建的高效空间-光谱Transformer，以及一个频域融合模块以保证特征一致性。 在模拟和真实数据上的实验表明，我们的方法在重建质量和效率方面优于之前最先进的方法。 显示英文摘要

摘要： 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.