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] Wang Jian-Yu, Shu Rong, Liu Yin-nian et al[M]. Science Press.
[2] Wu P Z. Characteristics and applications of satellite-borne hyperspectral imaging spectrometer[J]. Remote Sensing of Land Resources, 10(1999).
[3] Ouyang Z Y. Ouyang Ziyuan: Scientific objectives of China's lunar exploration project[J]. Proceedings of the Chinese Academy of Sciences.
[4] Dun X, Fu Q, Li H T et al. Advances in the frontiers of computational imaging[J]. Chinese Journal of Image Graphics, 27, 37(2022).
[5] Donoho D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 52, 1289-1306(2006).
[22] Chen Z, Cheng J. Proximal gradient descent unfolding dense-spatial spectral-attention transformer for compressive spectral imaging[J]. arXiv preprint(2023).
[29] Lehtinen J, Munkberg J, Hasselgren J et al. Noise2Noise: learning image restoration without clean data[J]. 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: Chun-Lai LI (lichunlai@mail.sitp.ac.cn), Shi-Jie LIU (liushijie@ucas.ac.cn)