  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.
