HURDNet: Heterogeneous UNet Structure With Range-Null Space Decomposition for Hyperspectral Image Reconstruction

Yeheng Zhu, Zhijian Wu, Jun Li, Jianhua Xu

Abstract: Hyperspectral image (HSI) reconstruction aims to recover a 3D HSI from its degraded 2D measurement. Despite achieving significant progress, most methods do not consider the data-consistency condition, leading to biased reconstructed results. Besides, current UNet structures mainly utilize homogeneous modules for feature reconstruction without fully exploring the heterogeneous structures. To overcome the aforementioned drawbacks, we propose a heterogeneous UNet structure with range-null space decomposition for HSI reconstruction termed HURDNet. Specifically, we leverage range-null space decomposition for data-consistency calibration, whilst design a precise average calibration pseudo-inverse operator (ACPIO) for finely modeling the inverse HSI degradation process, such that 3D HSIs can be precisely reconstructed. In addition, we explore heterogeneous structure design in UNet architecture, and develop a pixel-wise long-term non-local Transformer structure (PLNT) particularly for feature reconstruction. Numerous experimental results demonstrate that our HURDNet achieves state-of-the-art performance on both simulation and real HSI datasets.