Journal of Infrared and Millimeter Waves, Volume. 43, Issue 6, 847(2024)
Deep plug-and-play self-supervised neural networks for spectral snapshot compressive imaging
[1] Jian-Yu Wang, Rong Shu, Yin-nian Liu et al. Science Press.
[2] P Z Wu. Characteristics and applications of satellite-borne hyperspectral imaging spectrometer. Remote Sensing of Land Resources, 10(1999).
[3] Z Y Ouyang. Ouyang Ziyuan: Scientific objectives of China's lunar exploration project. Proceedings of the Chinese Academy of Sciences.
[4] X Dun, Q Fu, H T Li et al. Advances in the frontiers of computational imaging. Chinese Journal of Image Graphics, 27, 37(2022).
[22] Z Chen, J Cheng. Proximal gradient descent unfolding dense-spatial spectral-attention transformer for compressive spectral imaging. arXiv preprint(2023).
[29] J Lehtinen, J Munkberg, J Hasselgren et al. Noise2Noise: learning image restoration without clean data. arXiv e-prints, 2018(1803).
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Xing-Yu ZHANG, Shou-Zheng ZHU, Tian-Shu ZHOU, Hong-Xing QI, Jian-Yu WANG, Chun-Lai LI, Shi-Jie LIU. Deep plug-and-play self-supervised neural networks for spectral snapshot compressive imaging[J]. Journal of Infrared and Millimeter Waves, 2024, 43(6): 847
Category: Interdisciplinary Research on Infrared Science
Received: Feb. 29, 2024
Accepted: --
Published Online: Dec. 13, 2024
The Author Email: LI Chun-Lai (lichunlai@mail.sitp.ac.cn), LIU Shi-Jie (liushijie@ucas.ac.cn)