Lightweight Accelerated Unfolding Network With Collaborative Attention for Snapshot Spectral Compressive Imaging

Abstract

In coded aperture snapshot spectral imaging (CASSI) systems, deep unfolding networks (DUNs) have made significant strides in recovering 3D hyperspectral images (HSIs) from a single 2D measurement. However, the inherent nonlinearity and ill-posed nature of HSI reconstruction continue to challenge existing methods in terms of accuracy and stability. To address these challenges, we propose a lightweight collaborative attention-enhanced accelerated unfolding network (${\mathrm{CA}}^2\mathrm{UN}$), which integrates a DUN framework with a streamlined prior extractor. Our integrated approach introduces a generically accelerated half-quadratic splitting algorithm (A-HQS) for degradation estimation, overcoming the limitations of first-order optimization and enabling effective long-range dependency modeling. Within the prior extractor, we introduce cross-convergence attention, facilitating iterative information exchange between local and non-local Transformers to capture holistic features and enhance inductive capacity. Notably, the concept of collaborative cross-convergence is embedded throughout all submodules, ensuring effective information flow. The proposed ${\mathrm{CA}}^2\mathrm{UN}$ not only accelerates the convergence of spectral reconstruction, but also fully exploits compressed spatial-spectral information. Numerical and visual comparisons on both synthetic and real datasets demonstrate the superior performance of this approach. Comparisons on both synthetic and real datasets illustrate the superiority of this approach. The source code is available at https://github.com/Mengjie-s/CA2UN.