From Local to Global: A Progressive Reconstruction Network for Diffractive Snapshot Spectral Imaging

Diffractive Snapshot Spectral Imaging (DSSI) encodes spec-tral and spatial information into a single 2D measurement via a diffrac-tive optical element (DOE), offering a compact and light-efficient al-ternative to conventional Coded-Aperture Snapshot Spectral Imaging(CASSI). However, the spatially uneven coding of DSSI, which is mainlyconcentrated in texture-rich regions, has led existing reconstruction net-works to rely heavily on global attention modules to propagate spectralfeatures into texture-less regions, making them short-sighted in locallycoded areas and computationally redundant. To address these challenges,we propose Local-to-Global Spectral Reconstruction Network (LGSR-Net), a two-stage progressive framework comprising complementary lo-cal and global subnetworks. The local part extracts information fromtexture-rich regions under direct supervision from effectively coded ar-eas, while the global part models long-range dependencies to facilitateglobal information flow. We introduce a spatially guided channel atten-tion module to emphasize informative regions and an edge-aware gatedfusion mechanism to integrate local features into the global subnetwork’sdecoders. Moreover, a coding-aware sample selection strategy and a tai-lored loss function are designed to improve training efficiency and re-construction fidelity. Extensive experiments on both simulated and realDSSI datasets show that LGSRNet achieves state-of-the-art reconstruc-tion performance with significantly lower computational cost and latency.