{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T06:37:20Z","timestamp":1771051040536,"version":"3.50.1"},"reference-count":74,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,11,9]],"date-time":"2020-11-09T00:00:00Z","timestamp":1604880000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/S028730\/1"],"award-info":[{"award-number":["EP\/S028730\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Spectral reconstruction algorithms recover spectra from RGB sensor responses. Recent methods\u2014with the very best algorithms using deep learning\u2014can already solve this problem with good spectral accuracy. However, the recovered spectra are physically incorrect in that they do not induce the RGBs from which they are recovered. Moreover, if the exposure of the RGB image changes then the recovery performance often degrades significantly\u2014i.e., most contemporary methods only work for a fixed exposure. In this paper, we develop a physically accurate recovery method: the spectra we recover provably induce the same RGBs. Key to our approach is the idea that the set of spectra that integrate to the same RGB can be expressed as the sum of a unique fundamental metamer (spanned by the camera\u2019s spectral sensitivities and linearly related to the RGB) and a linear combination of a vector space of metameric blacks (orthogonal to the spectral sensitivities). Physically plausible spectral recovery resorts to finding a spectrum that adheres to the fundamental metamer plus metameric black decomposition. To further ensure spectral recovery that is robust to changes in exposure, we incorporate exposure changes in the training stage of the developed method. In experiments we evaluate how well the methods recover spectra and predict the actual RGBs and RGBs under different viewing conditions (changing illuminations and\/or cameras). The results show that our method generally improves the state-of-the-art spectral recovery (with more stabilized performance when exposure varies) and provides zero colorimetric error. Moreover, our method significantly improves the color fidelity under different viewing conditions, with up to a 60% reduction in some cases.<\/jats:p>","DOI":"10.3390\/s20216399","type":"journal-article","created":{"date-parts":[[2020,11,9]],"date-time":"2020-11-09T19:08:29Z","timestamp":1604948909000},"page":"6399","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Physically Plausible Spectral Reconstruction"],"prefix":"10.3390","volume":"20","author":[{"given":"Yi-Tun","family":"Lin","sequence":"first","affiliation":[{"name":"School of Computing Sciences, University of East Anglia, Norwich NR4 7TJ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Graham D.","family":"Finlayson","sequence":"additional","affiliation":[{"name":"School of Computing Sciences, University of East Anglia, Norwich NR4 7TJ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3574","DOI":"10.1109\/TIP.2014.2329767","article-title":"Hyperspectral image segmentation using a new spectral unmixing-based binary partition tree representation","volume":"23","author":"Veganzones","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2381","DOI":"10.1109\/JSTARS.2015.2388577","article-title":"Spectral\u2013spatial classification of hyperspectral data based on deep belief network","volume":"8","author":"Chen","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1109\/TGRS.2014.2358934","article-title":"A survey on spectral\u2014spatial classification techniques based on attribute profiles","volume":"53","author":"Ghamisi","year":"2014","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2438","DOI":"10.1109\/LGRS.2015.2482520","article-title":"Unsupervised spectral-spatial feature learning with stacked sparse autoencoder for hyperspectral imagery classification","volume":"12","author":"Tao","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5795","DOI":"10.3390\/rs6065795","article-title":"Spectral-spatial classification of hyperspectral image based on kernel extreme learning machine","volume":"6","author":"Chen","year":"2014","journal-title":"Remote. Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1725","DOI":"10.1109\/LGRS.2015.2421813","article-title":"Principal component reconstruction error for hyperspectral anomaly detection","volume":"12","author":"Jablonski","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1109\/TMI.2016.2600249","article-title":"Tensor-based dictionary learning for spectral CT reconstruction","volume":"36","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Med Imaging"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1109\/TCI.2016.2609414","article-title":"Spectral CT reconstruction with image sparsity and spectral mean","volume":"2","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_9","unstructured":"Deering, M. (2005). Multi-Spectral Color Correction. (6,950,109), U.S. Patent."},{"key":"ref_10","unstructured":"Abrardo, A., Alparone, L., Cappellini, I., and Prosperi, A. (1999, January 24\u201328). Color constancy from multispectral images. Proceedings of the International Conference on Image Processing, Kobe, Japan."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1364\/JOSAA.22.001231","article-title":"Characterization of trichromatic color cameras by using a new multispectral imaging technique","volume":"22","author":"Cheung","year":"2005","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Lam, A., and Sato, I. (2013, January 23\u201328). Spectral modeling and relighting of reflective-fluorescent scenes. Proceedings of the Conference on Computer Vision and Pattern Recognition, Portland, OR, USA.","DOI":"10.1109\/CVPR.2013.191"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"8461","DOI":"10.1364\/AO.56.008461","article-title":"Self-training-based spectral image reconstruction for art paintings with multispectral imaging","volume":"56","author":"Xu","year":"2017","journal-title":"Appl. Opt."},{"key":"ref_14","unstructured":"Gat, N. (2000, January 26). Imaging spectroscopy using tunable filters: A review. Proceedings of the Wavelet Applications VII, International Society for Optics and Photonics, Orlando, FL, USA."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/S0034-4257(98)00064-9","article-title":"Imaging spectroscopy and the airborne visible\/infrared imaging spectrometer (AVIRIS)","volume":"65","author":"Green","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2423","DOI":"10.1109\/TPAMI.2011.80","article-title":"A prism-mask system for multispectral video acquisition","volume":"33","author":"Cao","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1754","DOI":"10.1364\/JOSAA.32.001754","article-title":"Snapshot colored compressive spectral imager","volume":"32","author":"Correa","year":"2015","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6174","DOI":"10.1109\/TIP.2018.2867273","article-title":"Multi-resolution compressive spectral imaging reconstruction from single pixel measurements","volume":"27","author":"Garcia","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1896","DOI":"10.1109\/TIP.2014.2310125","article-title":"Colored coded aperture design by concentration of measure in compressive spectral imaging","volume":"23","author":"Arguello","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"6332","DOI":"10.1364\/AO.56.006332","article-title":"Coded aperture design in compressive spectral imaging based on side information","volume":"56","author":"Galvis","year":"2017","journal-title":"Appl. Opt."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1145\/2661229.2661262","article-title":"Spatial-spectral encoded compressive hyperspectral imaging","volume":"33","author":"Lin","year":"2014","journal-title":"ACM Trans. Graph."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1364\/JOSAA.32.000080","article-title":"DMD-based implementation of patterned optical filter arrays for compressive spectral imaging","volume":"32","author":"Rueda","year":"2015","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Guo, H., Ma, Z., Cao, X., Yue, T., and Hu, X. (2019, January 15\u201320). Hyperspectral Imaging With Random Printed Mask. Proceedings of the Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01039"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Shrestha, R., Hardeberg, J.Y., and Khan, R. (2011, January 25\u201327). Spatial arrangement of color filter array for multispectral image acquisition. Proceedings of the Sensors, Cameras, and Systems for Industrial, Scientific, and Consumer Applications XII, International Society for Optics and Photonics, San Francisco, CA, USA.","DOI":"10.1117\/12.872253"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"7173","DOI":"10.1364\/OE.20.007173","article-title":"Hybrid-resolution multispectral imaging using color filter array","volume":"20","author":"Murakami","year":"2012","journal-title":"Opt. Express"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Mihoubi, S., Losson, O., Mathon, B., and Macaire, L. (2015, January 10\u201313). Multispectral demosaicing using intensity-based spectral correlation. Proceedings of the International Conference on Image Processing Theory, Tools and Applications, Orleans, France.","DOI":"10.1109\/IPTA.2015.7367188"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2368","DOI":"10.1109\/TIP.2008.2006605","article-title":"Multispectral filter-wheel cameras: Geometric distortion model and compensation algorithms","volume":"17","author":"Brauers","year":"2008","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, L., Xiong, Z., Gao, D., Shi, G., Zeng, W., and Wu, F. (2015, January 7\u201312). High-speed hyperspectral video acquisition with a dual-camera architecture. Proceedings of the Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299128"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Park, J.I., Lee, M.H., Grossberg, M.D., and Nayar, S.K. (2007, January 14\u201321). Multispectral imaging using multiplexed illumination. Proceedings of the International Conference on Computer Vision, Rio De Janeiro, Brazil.","DOI":"10.1109\/ICCV.2007.4409090"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Hirai, K., Tanimoto, T., Yamamoto, K., Horiuchi, T., and Tominaga, S. (2013, January 2\u20135). An LED-based spectral imaging system for surface reflectance and normal estimation. Proceedings of the International Conference on Signal-Image Technology & Internet-Based Systems, Kyoto, Japan.","DOI":"10.1109\/SITIS.2013.78"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Shrestha, R., Hardeberg, J.Y., and Mansouri, A. (2011, January 24\u201325). One-shot multispectral color imaging with a stereo camera. Proceedings of the Digital Photography VII, International Society for Optics and Photonics, San Francisco, CA, USA.","DOI":"10.1117\/12.872428"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Takatani, T., Aoto, T., and Mukaigawa, Y. (2017, January 21\u201326). One-shot hyperspectral imaging using faced reflectors. Proceedings of the Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.288"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2444","DOI":"10.1364\/JOSAA.25.002444","article-title":"Evaluation and unification of some methods for estimating reflectance spectra from RGB images","volume":"25","author":"Heikkinen","year":"2008","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Connah, D., and Hardeberg, J. (2005, January 17). Spectral recovery using polynomial models. Proceedings of the Color Imaging X: Processing, Hardcopy, and Applications, International Society for Optics and Photonics, San Jose, CA, USA.","DOI":"10.1117\/12.586315"},{"key":"ref_35","unstructured":"Lin, Y., and Finlayson, G. (2019, January 21\u201325). Exposure Invariance in Spectral Reconstruction from RGB Images. Proceedings of the Color and Imaging Conference, Society for Imaging Science and Technology, Paris, France."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Nguyen, R., Prasad, D., and Brown, M. (2014, January 6\u201312). Training-based spectral reconstruction from a single RGB image. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10584-0_13"},{"key":"ref_37","unstructured":"Aeschbacher, J., Wu, J., and Timofte, R. (2017, January 22\u201329). In defense of shallow learned spectral reconstruction from RGB images. Proceedings of the International Conference on Computer Vision, Venice, Italy."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1364\/JOSAA.3.000029","article-title":"Color constancy: A method for recovering surface spectral reflectance","volume":"3","author":"Maloney","year":"1986","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1002\/col.20431","article-title":"Reconstruction of reflectance spectra using weighted principal component analysis","volume":"33","author":"Agahian","year":"2008","journal-title":"Color Res. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1002\/col.20341","article-title":"Image-based spectral reflectance reconstruction using the matrix R method","volume":"32","author":"Zhao","year":"2007","journal-title":"Color Res. Appl."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1393","DOI":"10.1364\/JOSAA.14.001393","article-title":"Bayesian color constancy","volume":"14","author":"Brainard","year":"1997","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1814","DOI":"10.1364\/JOSAA.23.001814","article-title":"Metamer-set-based approach to estimating surface reflectance from camera RGB","volume":"23","author":"Morovic","year":"2006","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1868","DOI":"10.1364\/JOSAA.27.001868","article-title":"Reflectance spectra recovery from tristimulus values by adaptive estimation with metameric shape correction","volume":"27","author":"Bianco","year":"2010","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1109\/TSP.2007.907838","article-title":"From color sensor space to feasible reflectance spectra","volume":"56","author":"Zuffi","year":"2008","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Arad, B., and Ben-Shahar, O. (2016, January 11\u201314). Sparse recovery of hyperspectral signal from natural RGB images. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46478-7_2"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Shi, Z., Chen, C., Xiong, Z., Liu, D., and Wu, F. (2018, January 2\u20136). Hscnn+: Advanced cnn-based hyperspectral recovery from RGB images. Proceedings of the Conference on Computer Vision and Pattern Recognition Workshops, Perth, Australia.","DOI":"10.1109\/CVPRW.2018.00139"},{"key":"ref_47","unstructured":"Arad, B., Ben-Shahar, O., and Timofte, R. (2018, January 18\u201322). NTIRE 2018 challenge on spectral reconstruction from RGB images. Proceedings of the Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA."},{"key":"ref_48","unstructured":"Arad, B., Timofte, R., Ben-Shahar, O., Lin, Y., and Finlayson, G. (2020, January 14\u201319). NTIRE 2020 challenge on spectral reconstruction from an RGB image. Proceedings of the Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"107394","DOI":"10.1016\/j.sigpro.2019.107394","article-title":"CNN based spectral super-resolution of remote sensing images","volume":"169","author":"Arun","year":"2020","journal-title":"Signal Process."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Li, J., Wu, C., Song, R., Li, Y., and Liu, F. (2020, January 14\u201319). Adaptive weighted attention network with camera spectral sensitivity prior for spectral reconstruction from RGB images. Proceedings of the Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00239"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Joslyn Fubara, B., Sedky, M., and Dyke, D. (2020, January 14\u201319). RGB to Spectral Reconstruction via Learned Basis Functions and Weights. Proceedings of the Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00248"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Chakrabarti, A., and Zickler, T. (2011, January 20\u201325). Statistics of real-world hyperspectral images. Proceedings of the Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995660"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Po, L.M., Yan, Q., Liu, W., and Lin, T. (2020, January 14\u201319). Hierarchical regression network for spectral reconstruction from RGB images. Proceedings of the Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00219"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1002\/col.20070","article-title":"The CIEDE2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations","volume":"30","author":"Sharma","year":"2005","journal-title":"Color Res. Appl."},{"key":"ref_55","unstructured":"Hardeberg, J.Y. (2002, January 2\u20135). On the spectral dimensionality of object colours. Proceedings of the Conference on Colour in Graphics, Imaging, and Vision, Society for Imaging Science and Technology, Poitiers, France."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1364\/JOSAA.14.001007","article-title":"Linear bases for representation of natural and artificial illuminants","volume":"14","author":"Romero","year":"1997","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Lee, T.W., Wachtler, T., and Sejnowski, T.J. (2000, January 15\u201317). The spectral independent components of natural scenes. Proceedings of the International Workshop on Biologically Motivated Computer Vision, Seoul, Korea.","DOI":"10.1007\/3-540-45482-9_53"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1905","DOI":"10.1364\/JOSAA.9.001905","article-title":"Linear models of surface and illuminant spectra","volume":"9","author":"Marimont","year":"1992","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1364\/JOSAA.6.000318","article-title":"Characteristic spectra of Munsell colors","volume":"6","author":"Parkkinen","year":"1989","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_60","unstructured":"Strang, G. (2016). Introduction to Linear Algebra, Wellesley-Cambridge Press. [5th ed.]."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1364\/JOSAA.22.000810","article-title":"Metamer sets","volume":"22","author":"Finlayson","year":"2005","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"836","DOI":"10.1134\/1.2135863","article-title":"Optical properties of the subcutaneous adipose tissue in the spectral range 400\u20132500 nm","volume":"99","author":"Bashkatov","year":"2005","journal-title":"Opt. Spectrosc."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1552","DOI":"10.1109\/TPAMI.2003.1251148","article-title":"Face recognition in hyperspectral images","volume":"25","author":"Pan","year":"2003","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1109\/TPAMI.1987.4767868","article-title":"The synthesis and analysis of color images","volume":"1","author":"Wandell","year":"1987","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"4311","DOI":"10.1109\/TSP.2006.881199","article-title":"K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation","volume":"54","author":"Aharon","year":"2006","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_66","unstructured":"Tikhonov, A., Goncharsky, A., Stepanov, V., and Yagola, A. (2013). Numerical Methods for the Solution of Ill-Posed Problems, Springer Science & Business Media."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Sammut, C., and Webb, G.I. (2010). Overfitting. Encyclopedia of Machine Learning, Springer.","DOI":"10.1007\/978-0-387-30164-8"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_69","unstructured":"Cheney, W., and Kincaid, D. (2009). Linear Algebra: Theory and Applications, Jones & Bartlett Learning."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"537","DOI":"10.2307\/1422186","article-title":"Metameric color stimuli, fundamental metamers, and Wyszecki\u2019s metameric blacks","volume":"95","author":"Cohen","year":"1982","journal-title":"Am. J. Psychol."},{"key":"ref_71","unstructured":"Commission Internationale de l\u2019Eclairage (1964). CIE Proceedings (1964) Vienna Session, Committee Report E-1.4, Commission Internationale de l\u2019Eclairage."},{"key":"ref_72","unstructured":"Commission Internationale de l\u2019Eclairage (1932). Commission Internationale de L\u2019eclairage Proceedings (1931), Cambridge University."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1002\/j.1520-6378.1977.tb00104.x","article-title":"The CIE 1976 color-difference formulae","volume":"2","author":"Robertson","year":"1977","journal-title":"Color Res. Appl."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"S\u00fcsstrunk, S., Buckley, R., and Swen, S. (1999, January 16\u201319). Standard RGB color spaces. Proceedings of the Color and Imaging Conference, Society for Imaging Science and Technology, Scottsdale, AZ, USA.","DOI":"10.2352\/CIC.1999.7.1.art00024"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/21\/6399\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:31:18Z","timestamp":1760178678000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/21\/6399"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,9]]},"references-count":74,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["s20216399"],"URL":"https:\/\/doi.org\/10.3390\/s20216399","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,9]]}}}