[1] LI J, WU C, SONG R et al. Adaptive weighted attention network with camera spectral sensitivity prior for spectral reconstruction from RGB images[C], 462-463(2020).

[2] ARAD B, BEN-SHAHAR O. Sparse recovery of hyperspectral signal from natural RGB images[C], 19-34(2016).

[3] AESCHBACHER J, WU J, TIMOFTE R. In defense of shallow learned spectral reconstruction from RGB images[C], 471-479(2017).

[4] AKHTAR N, MIAN A. Hyperspectral recovery from RGB images using Gaussian processes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 100-113(2020).

[5] GALLIANI S, LANARAS C, MARMANIS D et al. Learned spectral super-resolution[J]. arXiv preprint(2017).

[6] HE J, LI J, YUAN Q et al. Spectral response function-guided deep optimization-driven network for spectral super-resolution[J]. IEEE Transactions on Neural Networks and Learning Systems, 33, 4213-4227(2021).

[7] CAI Y, LIN J, LIN Z et al. Mst++: multi-stage spectral-wise transformer for efficient spectral reconstruction[C], 745-755(2022).

[8] HE J, YUAN Q, LI J et al. PoNet: a universal physical optimization-based spectral super-resolution network for arbitrary multispectral images[J]. Information Fusion, 80, 205-225(2022).

[9] FAN Yu, WANG Huiqin, WANG Ke et al. Multi-output least-squares SVR spectral reflectance reconstruction method based on adaptive optimization in multi-scene[J]. Acta Photonica Sinica, 51, 0230003(2022).