[1] Aizu Y, Maeda T, Kuwahara T et al. Skin image reconstruction using Monte Carlo based color generation[J]. Proceedings of SPIE, 7851, 78510N(2010). http://www.ixueshu.com/api/search/info/8345171e8e4bca8c5f86ef2893e7b3ce318947a18e7f9386.html
[2] Kim T, Visbal-Onufrak M A, Konger R L et al. Data-driven imaging of tissue inflammation using RGB-based hyperspectral reconstruction toward personal monitoring of dermatologic health[J]. Biomedical Optics Express, 8, 5282-5296(2017).
[3] Grabowski B, Masarczyk W, Głomb P et al. Automatic pigment identification from hyperspectral data[J]. Journal of Cultural Heritage, 31, 1-12(2018). http://www.sciencedirect.com/science/article/pii/S1296207417306544
[4] Yang Y R, Bu Y, Xu J H et al. Measurement of surface defects of optical elements based on spectral estimation and multispectral technique[J]. Chinese Journal of Lasers, 46, 0904002(2019).
[5] Wang Y N, Zhu D N, Wang H Q et al. Multispectral image classification of mural pigments based on convolutional neural network[J]. Laser & Optoelectronics Progress, 56, 221001(2019).
[6] Yang L, Wang H Q, Wang K et al. Spectral information unmixing of mixed pigment based on clustering optimization FastICA algorithm[J]. Acta Optica Sinica, 40, 0530001(2020).
[7] Ribes A, Schmitt F. Linear inverse problems in imaging[J]. IEEE Signal Processing Magazine, 25, 84-99(2008). http://ieeexplore.ieee.org/document/4545851
[8] Li Y Q, Wang C, Zhao J Y. Locally linear embedded sparse coding for spectral reconstruction from RGB images[J]. IEEE Signal Processing Letters, 25, 363-367(2018). http://ieeexplore.ieee.org/document/8116687
[9] Rueda H, Lau D, Arce G R. Multi-spectral compressive snapshot imaging using RGB image sensors[J]. Optics Express, 23, 12207-12221(2015). http://www.ncbi.nlm.nih.gov/pubmed/25969307
[10] Toque J A, Murayama Y, Ide-Ektessabi A. Pigment identification based on spectral reflectance reconstructed from RGB images for cultural heritage investigations[J]. Proceedings of SPIE, 7531, 75310K(2010). http://spie.org/Publications/Proceedings/Paper/10.1117/12.840001
[11] Blasinski H, Breneman J, Farrell J. A model for estimating spectral properties of water from RGB images[C]. //2014 IEEE International Conference on Image Processing (ICIP), October 27-30, 2014, Paris, France., 610-614(2014).
[12] Kim T H. Hyperspectral image reconstruction from RGB data and its biomedical applications[D], 115-132(2017).
[13] Connah D R, Hardeberg J Y. Spectral recovery using polynomial models[J]. Proceedings of SPIE, 5667, 65-75(2005).
[14] Xiao K D, Zhu Y T, Li C J et al. Improved method for skin reflectance reconstruction from camera images[J]. Optics Express, 24, 14934-14950(2016).
[15] Zhang X D, Wang Q, Li J C et al. Estimating spectral reflectance from camera responses based on CIE XYZ tristimulus values under multi-illuminants[J]. Color Research & Application, 42, 68-77(2017). http://smartsearch.nstl.gov.cn/paper_detail.html?id=6ae6e96e69d9ca0a68313b6892e7e2d9
[16] Liang J X, Wan X X. Optimized method for spectral reflectance reconstruction from camera responses[J]. Optics Express, 25, 28273(2017). http://adsabs.harvard.edu/abs/2017OExpr..2528273L
[17] Finlayson G D, Mackiewicz M, Hurlbert A. Color correction using root-polynomial regression[J]. IEEE Transactions on Image Processing, 24, 1460-1470(2015).
[18] Nakamura J. Image sensors and signal processing for digital still cameras[M], 78-84(2005).
[19] Harrington P. Machine learning in action[M], 160-162(2012).
[20] Jiang J, Liu D Y, Gu J W et al. What is the space of spectral sensitivity functions for digital color cameras?[C]. //2013 IEEE Workshop on Applications of Computer Vision (WACV), January 15-17, 2013, Clearwater Beach, FL, USA., 168-179(2013).
[21] Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection[C]. // International Joint Conference on Artificial Intelligence, August, 1995, San Francisco, CA, United States(1995).