[1] [1] 1郝博雅， 孙洁. 高光谱影像特性研究［J］.计算机工程与应用， 2014， 50（S1）： 230-233.HAOB Y， SUNJ. Research of hyper-spectral imaging characteristics［J］. Computer Engineering and Applications， 2014， 50（S1）： 230-233. （in Chinese）

[2] S OZKAN, B KAYA, G B AKAR. EndNet： sparse AutoEncoder network for endmember extraction and hyperspectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 57, 482-496(2019).

[3] M GOVENDER, K CHETTY, H BULCOCK. A review of hyperspectral remote sensing and its application in vegetation and water resource studies. Water SA, 33, 145-151(2009).

[4] D W J STEIN, S G BEAVEN, L E HOFF et al. Anomaly detection from hyperspectral imagery. IEEE Signal Processing Magazine, 19, 58-69(2002).

[5] D F BARBIN, G ELMASRY, D W SUN et al. Non-destructive determination of chemical composition in intact and minced pork using near-infrared hyperspectral imaging. Food Chemistry, 138, 1162-1171(2013).

[6] M E MARTIN, M B WABUYELE, K CHEN et al. Development of an advanced hyperspectral imaging （HSI） system with applications for cancer detection. Annals of Biomedical Engineering, 34, 1061-1068(2006).

[7] [7] 7李勇， 王珂， 张立保， 等. 多断层融合的肺CT肿瘤靶区超分辨率重建［J］. 光学 精密工程， 2010， 18（5）： 1212-1218.LIY， WANGK， ZHANGL B， et al. Super-resolution reconstruction of pulmonary nodules based on CT multi-section fusion［J］. Opt. Precision Eng.， 2010， 18（5）： 1212-1218. （in Chinese）

[8] B ARAD, O BEN-SHAHAR, R TIMOFTE et al. NTIRE 2018 challenge on spectral reconstruction from RGB images, 1042-104209(2018).

[9] B ARAD, R TIMOFTE, O BEN-SHAHAR et al. NTIRE 2020 challenge on spectral reconstruction from an RGB image, 1806-1822(2020).

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

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

[12] [12] 12韩玉兰， 赵永平， 王启松， 等. 稀疏表示下的噪声图像超分辨率重构［J］. 光学 精密工程， 2017， 25（6）： 1619-1626. doi: 10.3788/ope.20172506.1619HANY L， ZHAOY P， WANGQ S， et al. Reconstruction of super resolution for noise image under the sparse representation［J］. Opt. Precision Eng.， 2017， 25（6）： 1619-1626. （in Chinese）. doi: 10.3788/ope.20172506.1619

[13] [13] 13朱福珍， 刘越， 黄鑫， 等. 改进的稀疏表示遥感图像超分辨重建［J］. 光学 精密工程， 2019， 27（3）： 718-725. doi: 10.3788/ope.20192703.0718ZHUF ZH， LIUY， HUANGX， et al. Remote sensing image super-resolution based on improved sparse representation［J］. Opt. Precision Eng.， 2019， 27（3）： 718-725. （in Chinese）. doi: 10.3788/ope.20192703.0718

[14] Z W XIONG, Z SHI, H Q LI et al. HSCNN： CNN-based hyperspectral image recovery from spectrally undersampled projections, 518-525(2017).

[15] T STIEBEL, S KOPPERS, P SELTSAM et al. Reconstructing spectral images from RGB-images using a convolutional neural network, 1061-10615(2018).

[16] O RONNEBERGER, P FISCHER, T BROX. U-net： convolutional networks for biomedical image segmentation, 234-241(2015).

[17] S IOFFE, C SZEGEDY. Batch normalization： Accelerating deep network training by reducing internal covariate shift, 448-456(2015).

[18] I GOODFELLOW, J POUGET-ABADIE, M MIRZA et al. Generative adversarial networks. Communications of the ACM, 63, 139-144(2020).

[19] J J LI, C X WU, R SONG et al. Adaptive weighted attention network with camera spectral sensitivity prior for spectral reconstruction from RGB images, 1894-1903(2020).

[20] Y Z ZHAO, L M PO, Q YAN et al. Hierarchical regression network for spectral reconstruction from RGB images, 1695-1704(2020).

[21] H PENG, X M CHEN, J ZHAO. Residual pixel attention network for spectral reconstruction from RGB images, 2012-2020(2020).

[22] S H GAO, M M CHENG, K ZHAO et al. Res2Net： a new multi-scale backbone architecture. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43, 652-662(2021).

[23] J HU, L SHEN, S ALBANIE et al. Squeeze-and-excitation networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2011-2023(2020).

[24] [24] 24蔡体健， 彭潇雨， 石亚鹏， 等. 通道注意力与残差级联的图像超分辨率重建［J］. 光学 精密工程， 2021， 29（1）： 142-151. doi: 10.37188/OPE.20212901.0142CAIT J， PENGX Y， SHIY P， et al. Channel attention and residual concatenation network for image super-resolution［J］. Opt. Precision Eng.， 2021， 29（1）： 142-151. （in Chinese）. doi: 10.37188/OPE.20212901.0142

[25] V BADRINARAYANAN, A KENDALL, R CIPOLLA. SegNet： a deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 2481-2495(2017).

[26] K M HE, X Y ZHANG, S Q REN et al. Deep residual learning for image recognition, 770-778(2016).

[27] M Z ALOM, C YAKOPCIC, T M TAHA et al. Nuclei segmentation with recurrent residual convolutional neural networks based U-net （R2U-net）. OH, 228-233(2018).

[28] W Z SHI, J CABALLERO, F HUSZÁR et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network, 1874-1883(2016).

[29] [29] 29佘晖， 吕玮阁， 邱珏沁， 等. 商用数码相机的光谱灵敏度测量及评价［J］. 光学仪器， 2017， 39（5）： 15-21.SHEH， LÜW G， QIUJ Q， et al. Spectral sensitivity measurement and evaluation of commercial digital cameras［J］. Optical Instruments， 2017， 39（5）： 15-21. （in Chinese）

[30] A L MAAS, A Y HANNUN, A Y Ng. Rectifier nonlinearities improve neural network acoustic models, 30, 3(2013).