基于光谱重建技术的壁画颜色复原与评价
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摘要
为了在给定的照明和观察条件下,用相机响应信号重建物体表面光谱反射率,实现颜色的高精度复原,采用了多光谱成像技术采集物体表面的多光谱图像,使用主成分分析、R矩阵和正则化R矩阵方法进行了光谱反射率重建的理论研究,并对壁画色块颜色复原进行了实验验证,取得了壁画色块的重建光谱和颜色复原数据,同时对基于正则化R矩阵方法的壁画色块颜色复原结果进行了评价。结果表明,正则化R矩阵方法进行光谱重建的光谱精度和色度精度更高,与主成分分析和R矩阵方法相比,色差降低了0.0732,适应度系数提高了1.10%,均方根误差降低了0.0035,光谱匹配偏指数降低了0.0225。该方法能够满足高精度颜色再现的需要,适用于文物艺术品数字化存档、文物艺术品修复等领域。
Under given illumination and observation conditions, in order to reconstruct the spectral reflectance of the object surface from the camera response signal to achieve high-precision color reproduction, multi-spectral imaging technology was used to acquire multi-spectral image response of an object. The principal component analysis, matrix R and the new regularization matrix R method were used to analyze the theoretical analysis of spectral reflectance reconstruction. The results of the research were verified experimentally in the color reproduction of mural color blocks. The reconstructed spectral reflectance and color reproduction data of mural color blocks were obtained. At the same time, the color reproduction results of mural color blocks based on regularization matrix R method were evaluated. The results show that the regularization matrix R method is superior to the principal component analysis and matrix R method in the spectral accuracy and reconstructed accuracy. Compared with the principal component analysis and the matrix R method, the color difference is reduced by 0.0732, the fitness coefficient is increased by 1.10%, the root mean square error is reduced by 0.0035, and the spectral matching partial index is reduced by 0.0225. This method can meet the needs of high-precision color reproduction, which is suitable for digital archiving of cultural relic artwork, restoration of cultural relics and other fields.
Under given illumination and observation conditions, in order to reconstruct the spectral reflectance of the object surface from the camera response signal to achieve high-precision color reproduction, multi-spectral imaging technology was used to acquire multi-spectral image response of an object. The principal component analysis, matrix R and the new regularization matrix R method were used to analyze the theoretical analysis of spectral reflectance reconstruction. The results of the research were verified experimentally in the color reproduction of mural color blocks. The reconstructed spectral reflectance and color reproduction data of mural color blocks were obtained. At the same time, the color reproduction results of mural color blocks based on regularization matrix R method were evaluated. The results show that the regularization matrix R method is superior to the principal component analysis and matrix R method in the spectral accuracy and reconstructed accuracy. Compared with the principal component analysis and the matrix R method, the color difference is reduced by 0.0732, the fitness coefficient is increased by 1.10%, the root mean square error is reduced by 0.0035, and the spectral matching partial index is reduced by 0.0225. This method can meet the needs of high-precision color reproduction, which is suitable for digital archiving of cultural relic artwork, restoration of cultural relics and other fields.
引文
[1] ZOU J P, YANG J, LI H N, et al. Study of spectral reflectance reconstruction based on matrix R method[J]. Acta Optica Sinica, 2014, 34(s2):s233001(in Chinese).
[2] ZHENG C Y, GUO Zh H, JING L. Measurement of total viable count on chilled mutton surface based on hyperspectral imaging technique[J]. Laser Technology, 2015, 39(2):284-288(in Chinese).
[3] LI Y M, CHEN H J, LIU Ch J. Talking about the development of spectral color reproduction of printed manuscripts [J]. China Print, 2017,298(10):75-78(in Chinese).
[4] FAN L N, ZHOU Sh Sh. Online detection and evaluation of print co-lor reproduction quality based on iCAM[J]. Packaging Engineering, 2018, 39(1):196-201(in Chinese).
[5] HE S H, GAO Y, CHEN Q, et al. The set up of primary liner mixed space in spectral color reproduction[J]. Acta Optica Sinica, 2016, 36(3): 0333001(in Chinese).
[6] XIAO W, CHEN J Zh, YANG G G. Color printing and printing spectrum color separation method for metachromatic spectrum anti-counterfeiting: China, 104316469 A[P]. 2015-01-28(in Chinese).
[7] WU Q, WANG H, SHI Y L. Color reproduction quantitative analysis of color reflection holography[J]. Chinese Journal of Lasers, 2016,43(11): 1109001(in Chinese).
[8] HUANG H L, YI W N, DU L L, et al. Multi-spectral remote sensing image true color synthesis technique based on artificial target[J]. Infrared and Laser Engineering, 2016, 45(11):347-352 (in Chin-ese).
[9] ZENG Y, ZENG Y A, ZHANG N Y Sh, et al. A novel method to improve spectral detection capability of imaging spectrometers[J]. Laser Technology, 2018, 42(2):196-200(in Chinese).
[10] LONG Y Q, WANG H Q, WANG K, et al. Study on spectral reconstruction method based on optimized selected samples[J]. Imaging Science and Photochemistry, 2017, 35(1):88-96(in Chinese).
[11] HE S H, LIU Zh, CHEN Q. Research of spectral dimension reduction method based on matrix R theory[J]. Acta Optica Sinica, 2014, 34(2): 0233001(in Chinese).
[12] WANG J J, LIAO N F, WU W M, et al. Spectral reflectance reconstruction with nonlinear composite model of the metameric black[J]. Spectroscopy and Spectral Analysis, 2017, 37(3):704-709 (in Chinese).
[13] OU J X, LIU W Ch. Fitting of sphere point clouds by weighted total least squares based on IGGⅢ scheme[J]. Laser Technology, 2017, 41(5):749-753(in Chinese).
[14] WANG K, WANG H Q, LONG Y Q, et al. Spectral reflectance reconstruction based on dimension reduction regularization polynomials[J]. Laser & Optoelectronics Progress, 2018,55(5):053004(in Chinese).
[15] ONETO L, RIDELLA S, ANGUITA D. Tikhonov, Ivanov and Morozov regularization for support vector machine learning[J]. Machine Learning, 2016, 103(1):103-136.
[16] ZHENG H Y, HU F R. Design of narrowband guided-mode resonance filters in visible wavelength region[J]. Laser Technology, 2016, 40(1):118-121(in Chinese).
[17] LI F. Characteristics, mechanism and cleaning of mycosis diseases in ancient architecture murals[J]. Identification and Appreciation to Cultural Relics, 2016, 96(11):110-111(in Chinese).
[18] ZHANG F Zh, XU H S, FENG H. Spectral matching method for improving metamerism quality of LED daylight simulator[J]. China Illuminating Engineering Journal, 2017, 28(2):23-27(in Chin-ese).
[19] HUANG M, SHI Ch J, LI Z Y. Influences on observers color discrimination with normal color vision[J]. Acta Optica Sinica, 2016,36(9): 0933001 (in Chinese).
[20] HUANG M, CUI G H, LIU Y. Analysis of observers metamerism differences for different retinal cone visual responses [J]. Spectroscopy and Spectral Analysis, 2015, 35(10):2802-2809 (in Chin-ese).
[2] ZHENG C Y, GUO Zh H, JING L. Measurement of total viable count on chilled mutton surface based on hyperspectral imaging technique[J]. Laser Technology, 2015, 39(2):284-288(in Chinese).
[3] LI Y M, CHEN H J, LIU Ch J. Talking about the development of spectral color reproduction of printed manuscripts [J]. China Print, 2017,298(10):75-78(in Chinese).
[4] FAN L N, ZHOU Sh Sh. Online detection and evaluation of print co-lor reproduction quality based on iCAM[J]. Packaging Engineering, 2018, 39(1):196-201(in Chinese).
[5] HE S H, GAO Y, CHEN Q, et al. The set up of primary liner mixed space in spectral color reproduction[J]. Acta Optica Sinica, 2016, 36(3): 0333001(in Chinese).
[6] XIAO W, CHEN J Zh, YANG G G. Color printing and printing spectrum color separation method for metachromatic spectrum anti-counterfeiting: China, 104316469 A[P]. 2015-01-28(in Chinese).
[7] WU Q, WANG H, SHI Y L. Color reproduction quantitative analysis of color reflection holography[J]. Chinese Journal of Lasers, 2016,43(11): 1109001(in Chinese).
[8] HUANG H L, YI W N, DU L L, et al. Multi-spectral remote sensing image true color synthesis technique based on artificial target[J]. Infrared and Laser Engineering, 2016, 45(11):347-352 (in Chin-ese).
[9] ZENG Y, ZENG Y A, ZHANG N Y Sh, et al. A novel method to improve spectral detection capability of imaging spectrometers[J]. Laser Technology, 2018, 42(2):196-200(in Chinese).
[10] LONG Y Q, WANG H Q, WANG K, et al. Study on spectral reconstruction method based on optimized selected samples[J]. Imaging Science and Photochemistry, 2017, 35(1):88-96(in Chinese).
[11] HE S H, LIU Zh, CHEN Q. Research of spectral dimension reduction method based on matrix R theory[J]. Acta Optica Sinica, 2014, 34(2): 0233001(in Chinese).
[12] WANG J J, LIAO N F, WU W M, et al. Spectral reflectance reconstruction with nonlinear composite model of the metameric black[J]. Spectroscopy and Spectral Analysis, 2017, 37(3):704-709 (in Chinese).
[13] OU J X, LIU W Ch. Fitting of sphere point clouds by weighted total least squares based on IGGⅢ scheme[J]. Laser Technology, 2017, 41(5):749-753(in Chinese).
[14] WANG K, WANG H Q, LONG Y Q, et al. Spectral reflectance reconstruction based on dimension reduction regularization polynomials[J]. Laser & Optoelectronics Progress, 2018,55(5):053004(in Chinese).
[15] ONETO L, RIDELLA S, ANGUITA D. Tikhonov, Ivanov and Morozov regularization for support vector machine learning[J]. Machine Learning, 2016, 103(1):103-136.
[16] ZHENG H Y, HU F R. Design of narrowband guided-mode resonance filters in visible wavelength region[J]. Laser Technology, 2016, 40(1):118-121(in Chinese).
[17] LI F. Characteristics, mechanism and cleaning of mycosis diseases in ancient architecture murals[J]. Identification and Appreciation to Cultural Relics, 2016, 96(11):110-111(in Chinese).
[18] ZHANG F Zh, XU H S, FENG H. Spectral matching method for improving metamerism quality of LED daylight simulator[J]. China Illuminating Engineering Journal, 2017, 28(2):23-27(in Chin-ese).
[19] HUANG M, SHI Ch J, LI Z Y. Influences on observers color discrimination with normal color vision[J]. Acta Optica Sinica, 2016,36(9): 0933001 (in Chinese).
[20] HUANG M, CUI G H, LIU Y. Analysis of observers metamerism differences for different retinal cone visual responses [J]. Spectroscopy and Spectral Analysis, 2015, 35(10):2802-2809 (in Chin-ese).