  Existing leading methods for spectral reconstruction (SR) focus on designing
deeper or wider convolutional neural networks (CNNs) to learn the end-to-end
mapping from the RGB image to its hyperspectral image (HSI). These CNN-based
methods achieve impressive restoration performance while showing limitations in
capturing the long-range dependencies and self-similarity prior. To cope with
this problem, we propose a novel Transformer-based method, Multi-stage
Spectral-wise Transformer (MST++), for efficient spectral reconstruction. In
particular, we employ Spectral-wise Multi-head Self-attention (S-MSA) that is
based on the HSI spatially sparse while spectrally self-similar nature to
compose the basic unit, Spectral-wise Attention Block (SAB). Then SABs build up
Single-stage Spectral-wise Transformer (SST) that exploits a U-shaped structure
to extract multi-resolution contextual information. Finally, our MST++,
cascaded by several SSTs, progressively improves the reconstruction quality
from coarse to fine. Comprehensive experiments show that our MST++
significantly outperforms other state-of-the-art methods. In the NTIRE 2022
Spectral Reconstruction Challenge, our approach won the First place. Code and
pre-trained models are publicly available at
https://github.com/caiyuanhao1998/MST-plus-plus.
