Diffractive snapshot spectral imaging needs long-range dependency

Abstract

Hyperspectral imaging systems are valuable for various applications, but conventional spectral imaging systems often rely on time-consuming scanning mechanisms, hindering real-time imaging capabilities. Recently, diffractive spectral snapshot imaging (DSSI) systems have enabled compact and lightweight approach to capture spectral information from dynamic scenes. However, dedicated research on reconstructing DSSI-encoded images is still limited. Existing methods often adopt models originally designed for recovering hyperspectral images from clean RGB inputs, which are not fully suitable for DSSI systems. In this paper, we analyze the characteristics of DSSI systems and highlight the need for modeling long-range dependencies. By leveraging a novel state space model, we can capture these dependencies with relatively low computational cost. We further introduce a local-enhanced branch to the original vision state space module and build a local-enhanced long-range dependency block. Additionally, we propose a data selection strategy to improve optimization stability and reconstruction performance. Our approach achieves state-of-the-art performance on benchmark datasets and demonstrates superior reconstruction quality in real-world captured data.