@inproceedings{13ddc2d5416c45ff82cea24dce8c0d9c,
title = "Hysat++: Hybrid Spectral-Wise Attention Transformer for Skin Spectral Reconstruction",
abstract = "Hyperspectral imaging captures detailed spatial-spectral information across a wide range of wavelengths, offering a comprehensive and non-invasive method for skin analysis. In this paper, we propose a novel Hybrid Spectral-wise Attention Transformer (HySAT++) for facial skin scenarios, aimed at reconstructing skin hyperspectral images in the visible and near-infrared spectrum from facial skin multispectral data. HySAT++ simultaneously model spectral-wise similarity and particularity, and incorporates the Spectral Angle Mapper (SAM) loss as a part of the loss function. This work ranked first in the ICASSP 2024 SP Grand Challenge on Hyperspec-tral Skin Vision.",
keywords = "facial skin, Hyperspectral imaging, spectral reconstruction, transformer",
author = "Yuchen Wang and Hongyuan Wang and Jiang Xu and Chang Chen and Xue Hu and Fenglong Song and Lizhi Wang",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops, ICASSPW 2024 ; Conference date: 14-04-2024 Through 19-04-2024",
year = "2024",
doi = "10.1109/ICASSPW62465.2024.10627155",
language = "English",
series = "2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops, ICASSPW 2024 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "87--88",
booktitle = "2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops, ICASSPW 2024 - Proceedings",
address = "United States",
}