[4] [4] Arad B, Benshahar O. Sparse recovery of hyperspectral signal from natural RGB images[C]//European Conference on Computer Vision, 2016: 19-34.

[5] [5] Zhang Zexia, Chang Jun, Ren Hongxi, et al. Snapshot imaging spectrometer based on a microlens array[J]. Chinese Optics Letters, 2019, 17(1): 35-39.

[6] [6] Takatani T, Aoto T, Mukaigawa Y, et al. One-shot hyperspectral imaging using faced reflectors[C]//Computer Vision and Pattern Recognition, 2017: 2692-2700.

[7] [7] Oh S W, Brown M S, Pollefeys M, et al. Do it yourself hyperspectral imaging with everyday digital cameras[C]//Computer Vision and Pattern Recognition, 2016: 2461-2469.

[8] [8] Akhtar N, Shafait F, Mian A, et al. Hierarchical beta Process with gaussian process prior for hyperspectral image super resolution[C]//European Conference on Computer Vision, 2016: 103-120.

[9] [9] Jia Y, Zheng Y, Gu L, et al. From RGB to spectrum for natural scenes via manifold-based mapping[C]//International Conference on Computer Vision, 2017: 4715-4723.

[13] [13] Xiong Z, Shi Z, Li H, et al. HSCNN: CNN-based hyperspectral image recovery fromspectrally undersampled projections[C]//International Conference on Computer Vision, 2017: 518-525.

[14] [14] Shi Z, Chen C, Xiong Z, et al. HSCNN+: advanced CNN-based hyperspectral recovery from RGB images[C]// Computer Vision and Pattern Recognition, 2018: 939-947.

[15] [15] Alvarezgila A, De Weijer J V, Garrote E, et al. Adversarial networks for spatial context-aware spectral image reconstruction from RGB[C]//International Conference on Computer Vision, 2017: 480-490.

[16] [16] Chakrabarti A, Zickler T. Statistics of real-world hyperspectral images[C]//Computer Vision and Pattern Recognition, 2011: 193-200.