Transformer-based Residual Network for Hyperspectral Snapshot Compressive Reconstruction.

The core problem of hyperspectral snapshot compressive imaging is to achieve high-quality reconstruction from a single snapshot measurement. Existing reconstruction methods are mainly based on convolutional networks. Inspired by the exciting success of Transformer in high-level vision tasks, we introduce Transformer into hyperspectral snapshot compressive imaging, and propose the Transformer-based residual network to learn reconstruction mapping. Specifically, the proposed network cascades multiple basic modules, each of which consists of two local-enhanced window (Lewin) Transformer blocks and two residual blocks. The advantage of this basic module is ability to exploit both local feature maps and long-range dependencies for resonstruction. The Transformer blocks treat the feature map at each pixel location as a token and capture the spatial-spectral context of each pixel within a local window through self-attention, thus effectively improving the spectral fidelity of reconstructed hyperspectral images at modest computational cost. We conduct extensive experiments on both simulation and real data. The experimental results show that the proposed network is concise and effective, achieving the best reconstruction results compared with several state-of-the-art methods.