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
this https URL

通过使用基于 Transformer 的多阶段光谱智能变换器（MST++）及其包含的光谱注意块，该论文提出了一种新方法实现了显着优于其他最先进方法的重建结果，尤其适用于光谱重建挑战。