Spectral compressive imaging (SCI) is able to encode the high-dimensional hyperspectral image to a 2D measurement, and then uses algorithms to reconstruct the spatio-spectral data-cube. At present, the main bottleneck of SCI is the reconstruction algorithm, and the state-of-the-art (SOTA) reconstruction methods generally face the problem of long reconstruction time and/or poor detail recovery. In this paper, we propose a novel hybrid network module, namely CCoT (Convolution and Contextual Transformer) block, which can acquire the inductive bias ability of convolution and the powerful modeling ability of transformer simultaneously,and is conducive to improving the quality of reconstruction to restore fine details. We integrate the proposed CCoT block into deep unfolding framework based on the generalized alternating projection algorithm, and further propose the GAP-CCoT network. Through the experiments of extensive synthetic and real data, our proposed model achieves higher reconstruction quality ($>$2dB in PSNR on simulated benchmark datasets) and shorter running time than existing SOTA algorithms by a large margin. The code and models are publicly available at https://github.com/ucaswangls/GAP-CCoT.

翻译：光谱压缩成像（SCI）能够将高维高光谱图像编码为二维测量值，并通过算法重建空间-光谱数据立方体。当前，SCI的主要瓶颈在于重建算法，现有最先进的（SOTA）重建方法普遍面临重建时间长和/或细节恢复效果差的问题。本文提出一种新型混合网络模块——CCoT（卷积与上下文Transformer）块，该模块可同时获得卷积的归纳偏置能力和Transformer的强大建模能力，有利于提升重建质量以恢复精细细节。我们将所提出的CCoT块集成到基于广义交替投影算法的深度展开框架中，进一步提出GAP-CCoT网络。通过大量合成数据与真实数据的实验，我们提出的模型在重建质量（在模拟基准数据集上PSNR提升超过2dB）和运行时间方面均显著优于现有SOTA算法。代码和模型已公开于https://github.com/ucaswangls/GAP-CCoT。