|
|
|
|
|
|
| Research on Spectral Image Reconstruction Based on Nonlinear Spectral Dictionary Learning From Single RGB Image |
| ZUO Chu1, XIE De-hong2*, WAN Xiao-xia3 |
1. School of Light Industry and Food, Nanjing Forestry University, Nanjing 210037, China
2. College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China
3. Hubei Province Engineering Technical Center for Digitization and Virtual Roprodcuction of Color Information of Culture Relics, Wuhan University, Wuhan 430079, China
|
|
|
|
|
Abstract A nonlinear reconstruction method based on nonlinear spectral dictionary learning was proposed to solve the ill-posed problem of spectral image reconstruction from a single RGB image. In order to adapt to the linear and nonlinear data, the method firstly improves the nonlinear principal component analysis algorithm based on a modified self-association neural network model. It uses to learn the low-dimensional spectral dictionary from the training spectrum set, which is used in the inverse equation of spectral reconstruction to alleviate the ill condition. In addition, based on the spectral dictionary, the damped Gaussian Newton method combined with the truncated singular value decomposition regularization method is used further to alleviate the ill-posed problem of the nonlinear inversion, and the spectral image can be reconstructed from a single RGB image. In the experiment, two different spectral training sets, Munsell and Munsell+Pantone, were used to learn the spectral dictionary. Meanwhile, CAVE and UEA spectral image libraries were used for the spectral reconstruction tests. Compared with the existing methods, it is found that the average root means square error of CAVE and UEA spectral images reconstructed by this method under different spectral training sets were the lowest, which were 0.212 4, 0.255 4, 0.229 4 and 0.294 9 respectively. The standard deviations of root mean square error was close to the effect of the best method, which was 0.068 5, 0.084 7, 0.066 8 and 0.087 0 respectively. The results show that the method for reconstructing the spectral image from a single RGB image has advantages in accuracy and stability.
|
|
Received: 2021-04-27
Accepted: 2021-11-11
|
|
|
|
Corresponding Authors:
XIE De-hong
E-mail: dehong.xie@gmail.com
|
|
[1] Khan M J, Khan H S, Yousaf A, et al. IEEE Access, 2018, 6: 14118.
[2] Kuhns T, Messinger D W. Spectral Phenomenology of Historical Parchments and Inks to Aid Cultural Heritage Imaging System Development. VelezReyes M, Messinger D W. Proceedings of SPIE，2018.
[3] Del Pozo S, Rodriguez-Gonzalvez P, Sanchez-Aparicio L J, et al. Multispectral Imaging in Cultural Heritage Conservation. Hayes J, Ouimet C, Quintero M S, et al. International Archives of the Photogrammetry, Remote Sensing and SpatialInformation Sciences. Gottingen: Copernicus Gesellschaft MBH, 2017. 155.
[4] He Y, Jiao W, Shi Y, et al. IEEE Journal of Biomedical and Health Informatics, 2020, 24(2): 493.
[5] Brossard M, Marion R, Carrere V. Remote Sensing Letters, 2016, 7(6): 581.
[6] Zazi L, Boutaleb A, Guettouche M S. Arabian Journal of Geosciences, 2017, 10(11): 252.
[7] Thomas J, Lapray P, Gouton P, et al. Sensors, 2016, 16(7): 993.
[8] Ji X, Miao C, Zhang Y, et al. Optics Communications, 2017, 404(SI): 11.
[9] Wu R, Li Y, Xie X, et al. Sensors, 2019, 19(13): 2905.
[10] Neumaier A. SIAM Rev., 1998, 40: 636.
[11] Chou T, Hsieh C, Chen E. Color Research and Application, 2019, 44(4): 508.
[12] Sibson R. A Brief Description of Natural Neighbor Interpolation, John Wiley & Sons, 1981.
[13] Cao B, Liao N, Li Y, et al. Optical Engineering, 2017, 56(5): 053104.
[14] Arad B, Ben-Shahar O. Sparse Recovery of Hyperspectral Signal from Natural RGB Images. Leibe B, Matas J, Sebe N, et al. Lecture Notes in Computer Science，2016, 9911: 19.
[15] Oh S W, Brown M S, Pollefeys M, et al. Do It Yourself Hyperspectral Imaging with Everyday Digital Cameras. IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2016: 2461.
[16] Cao Q, Wan X, Li J, et al. Optical Review, 2016, 23(5): 753.
[17] Wu D, Tian J, Tang Y. Optical Review, 2014, 21(2): 117.
[18] Flinkman M, Laamanen H, Tuomela J, et al. Journal of the Optical Society of America A-Optics Image Science and Vision, 2013, 30(9): 1806.
[19] Chou Y, Cheung V, Luo M R, et al. Spectral Representation of Object Colours. Color and Imaging Conference Final Program and Proceedings，2012: 57.
[20] Rayat A, Amirshahi S H, Agahian F. Color Research and Application, 2014, 39(2): 136.
[21] Usui S, Nakauchi S, Nakano M. J. Opt. Soc. Am. A, 1992, 9(4): 516.
[22] LI Chan, WAN Xiao-xia, LIU Qiang, et al(李 婵，万晓霞，刘 强，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2016, 36(5): 1400.
[23] Barakzehi M, Amirshahi S H, Peyvandi S, et al. Journal of the Optical Society of America A-Optics Image Science and Vision, 2013, 30(9): 1862.
[24] Licciardi G, Chanussot J. European Journal of Remote Sensing, 2018, 51(1): 375.
[25] Snee R D, Marquardt D W. The American Statistician, 1984, 38(2): 83.
[26] Hsieh W W. Tellus Series A-Dynamic Meteorology and Oceanography, 2001, 53(5): 599.
[27] Björck Å. Numerical Methods for Least Squares Problems, 1996. 339
[28] Hansen P C. BIT Numerical Mathematics, 1987, 27(4): 534.
[29] Deidda G P, Fenu C, Rodriguez G. Inverse Problems, 2014, 30(12): 125014.
[30] Armijo L. Pacific Journal of Mathematics, 1966, 16(1): 1.
[31] https://www.pantone.com/nylon-brights-set.
[32] Yasuma F, Mitsunaga T, Iso D, et al. IEEE Transactions on Image Processing, 2010, 19(9): 2241.
[33] Finlayson G D, Hordley S D, Morovic P. CGIV 2004: 2nd European Conference on Color in Graphics, Imaging, and Vision，2004. 268.
|
| [1] |
YANG Cheng-en1, 2, LI Meng3, LU Qiu-yu2, WANG Jin-ling4, LI Yu-ting2*, SU Ling1*. Fast Prediction of Flavone and Polysaccharide Contents in
Aronia Melanocarpa by FTIR and ELM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 62-68. |
| [2] |
LIU Zhen1*, LIU Li2*, FAN Shuo2, ZHAO An-ran2, LIU Si-lu2. Training Sample Selection for Spectral Reconstruction Based on Improved K-Means Clustering[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 29-35. |
| [3] |
ZHENG Pei-chao, YIN Yi-tong, WANG Jin-mei*, ZHOU Chun-yan, ZHANG Li, ZENG Jin-rui, LÜ Qiang. Study on the Method of Detecting Phosphate Ions in Water Based on
Ultraviolet Absorption Spectrum Combined With SPA-ELM Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 82-87. |
| [4] |
HUANG You-ju1, TIAN Yi-chao2, 3*, ZHANG Qiang2, TAO Jin2, ZHANG Ya-li2, YANG Yong-wei2, LIN Jun-liang2. Estimation of Aboveground Biomass of Mangroves in Maowei Sea of Beibu Gulf Based on ZY-1-02D Satellite Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3906-3915. |
| [5] |
LIANG Jin-xing1, 2, 3, XIN Lei1, CHENG Jing-yao1, ZHOU Jing1, LUO Hang1, 3*. Adaptive Weighted Spectral Reconstruction Method Against
Exposure Variation[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3330-3338. |
| [6] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
| [7] |
FANG Zheng, WANG Han-bo. Measurement of Plastic Film Thickness Based on X-Ray Absorption
Spectrometry[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3461-3468. |
| [8] |
FU Gen-shen1, LÜ Hai-yan1, YAN Li-peng1, HUANG Qing-feng1, CHENG Hai-feng2, WANG Xin-wen3, QIAN Wen-qi1, GAO Xiang4, TANG Xue-hai1*. A C/N Ratio Estimation Model of Camellia Oleifera Leaves Based on
Canopy Hyperspectral Characteristics[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3404-3411. |
| [9] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
| [10] |
SUN Lin1, BI Wei-hong1, LIU Tong1, WU Jia-qing1, ZHANG Bao-jun1, FU Guang-wei1, JIN Wa1, WANG Bing2, FU Xing-hu1*. Identification Algorithm of Green Algae Using Airborne Hyperspectral and Machine Learning Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3637-3643. |
| [11] |
WANG Yu-chen1, 2, KONG Ling-qin1, 2, 3*, ZHAO Yue-jin1, 2, 3, DONG Li-quan1, 2, 3*, LIU Ming1, 2, 3, HUI Mei1, 2. Hyperspectral Reconstruction From RGB Images for Tissue Oxygen
Saturation Assessment[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3193-3201. |
| [12] |
MA Qian1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, CHENG Hui-zhu1, 2, ZHAO Yan-chun1, 2. Research on Classification of Heavy Metal Pb in Honeysuckle Based on XRF and Transfer Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2729-2733. |
| [13] |
LI Xin1, LIU Jiang-ping1, 2*, HUANG Qing1, HU Peng-wei1, 2. Optimization of Prediction Model for Milk Fat Content Based on Improved Whale Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2779-2784. |
| [14] |
CAI Jian-rong1, 2, HUANG Chu-jun1, MA Li-xin1, ZHAI Li-xiang1, GUO Zhi-ming1, 3*. Hand-Held Visible/Near Infrared Nondestructive Detection System for Soluble Solid Content in Mandarin by 1D-CNN Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2792-2798. |
| [15] |
YE Wen-chao1, LUO Shui-yang1, LI Jin-hao1, LI Zhao-rong1, FAN Zhi-wen1, XU Hai-tao1, ZHAO Jing1, LAN Yu-bin1, 2, DENG Hai-dong1*, LONG Yong-bing1, 2, 3*. Research on Classification Method of Hybrid Rice Seeds Based on the Fusion of Near-Infrared Spectra and Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2935-2941. |
|
|
|
|
