MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction

• URL: http://arxiv.org/abs/2204.07908v1
• Date: Sun, 17 Apr 2022 02:39:32 GMT
• Title: MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction
• Authors: Yuanhao Cai, Jing Lin, Zudi Lin, Haoqian Wang, Yulun Zhang, Hanspeter Pfister, Radu Timofte, Luc Van Gool
• Abstract summary: We propose a novel Transformer-based method, Multi-stage Spectral-wise Transformer (MST++) for efficient spectral reconstruction. In the NTIRE 2022 Spectral Reconstruction Challenge, our approach won the First place.
• Score: 148.26195175240923
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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