Shortwave-infrared(SWIR) spectral information, ranging from 1 {\mu}m to 2.5{\mu}m, overcomes the limitations of traditional color cameras in acquiring scene information. However, conventional SWIR hyperspectral imaging systems face challenges due to their bulky setups and low acquisition speeds. This work introduces a snapshot SWIR hyperspectral imaging system based on a metasurface filter and a corresponding filter selection method to achieve the lowest correlation coefficient among these filters. This system offers the advantages of compact size and snapshot imaging. We propose a novel inter and intra prior learning unfolding framework to achieve high-quality SWIR hyperspectral image reconstruction, which bridges the gap between prior learning and cross-stage information interaction. Additionally, We design an adaptive feature transfer mechanism to adaptively transfer the contextual correlation of multi-scale encoder features to prevent detailed information loss in the decoder. Experiment results demonstrate that our method can reconstruct hyperspectral images with high speed and superior performance over existing methods.

翻译：短波红外（SWIR）光谱信息（范围1μm至2.5μm）克服了传统彩色相机在获取场景信息方面的局限性。然而，传统的SWIR高光谱成像系统因其庞大的设备配置和较低的采集速度而面临挑战。本研究提出了一种基于超表面滤波器的快照式SWIR高光谱成像系统，并设计了一种相应的滤波器选择方法，以实现这些滤波器间最低的相关系数。该系统具有结构紧凑和快照成像的优势。我们提出了一种新颖的帧内与帧间先验学习展开框架，以实现高质量的SWIR高光谱图像重建，该框架弥合了先验学习与跨阶段信息交互之间的鸿沟。此外，我们设计了一种自适应特征传递机制，以自适应地传递多尺度编码器特征的上下文相关性，从而防止解码器中的细节信息丢失。实验结果表明，与现有方法相比，我们的方法能够以更快的速度重建高光谱图像，并展现出更优越的性能。