Spectral-attention transformer for single-pixel multispectral reconstruction

Zhou Xu; Xinyu Liu; Chang Wang; Yang Zhang; Qiangbo Zhang; Zhenrong Zheng

doi:10.1117/12.2687218

27 November 2023 Spectral-attention transformer for single-pixel multispectral reconstruction

Zhou Xu, Xinyu Liu, Chang Wang, Yang Zhang, Qiangbo Zhang, Zhenrong Zheng

Proceedings Volume 12767, Optoelectronic Imaging and Multimedia Technology X; 127671B (2023) https://doi.org/10.1117/12.2687218
Event: SPIE/COS Photonics Asia, 2023, Beijing, China

Conference Poster

Abstract

Compared with array detectors (such as CCD or CMOS), single pixel detectors have potential in invisible band and weak light applications to broaden the spectrum of spectral imaging. 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 spectral-attention transformer(SAT-net) method for single-pixel multispectral reconstruction. In addition, we introduce total variation (TV) to maintain the smooth structure of HSI. The experimental results of simulation and real data show that the proposed SAT-net is superior to other traditional algorithms based on compressive sensing(CS) methods.

Citation Download Citation

Zhou Xu, Xinyu Liu, Chang Wang, Yang Zhang, Qiangbo Zhang, and Zhenrong Zheng "Spectral-attention transformer for single-pixel multispectral reconstruction", Proc. SPIE 12767, Optoelectronic Imaging and Multimedia Technology X, 127671B (27 November 2023); https://doi.org/10.1117/12.2687218

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