Ukrainian Journal of Physical Optics 
Home page
 
 

Other articles 

in this issue
Reconstruction of spectral reflectance based on mixed weighting and local optimization

1Leihong Zhang, 1Runchu Xu, 1Shuangquan Lu, 1Liuhua Yang, 1Xiao Yuan, 2Kaiming Wang and 2Dawei Zhang

1College of Communication and Art Design, University of Shanghai for Science and Technology, Shanghai, 200093, China, *xrc1231@163.com
2School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China. * friedrich_suse@foxmail.com

Download this article

Abstract. High-fidelity colour reproduction can be performed through reconstructing a spectral reflectance of an object surface. The particular branches in need of this reproduction range from colour printing to artistic fields. In order to improve the reconstruction accuracy for the spectral reflectance, we suggest a spectral-reflectance reconstruction method based on a mixed weighting (MW). This method is a combination of a number of earlier techniques, namely a Wiener estimation method and so-called methods of weighting on a group with smaller colour difference and weighting on a group with smaller spectral reflectance error. Specifically, we have obtained the spectral estimation with higher reconstruction accuracy by weighting the reconstruction spectra obtained by different methods. The weights for these methods have been selected through minimizing the colour difference. The MW method makes a full use of advantages of the underlying methods. It reveals high accuracy and reduces the shortcomings of those methods. Our experimental results confirm that the MW method improves the reconstruction accuracy and the stability of spectral-reflectance data.

Keywords: colour difference, spectral reflectance error, spectral reflectance reconstruction, weighting, local optimization

UDC: 535.67
Ukr. J. Phys. Opt. 21 65-83
doi: 10.3116/16091833/21/2/65/2020
Received: 23.03.2020

Анотація. Високоточне відтворення кольорів можна здійснити за допомогою реконструкції спектрального відбивання поверхні предмета. Конкретні галузі, які потребують такого відтворення, варіюються від кольорового друку і аж до художнього мистецтва. Для підвищення точності реконструкції спектрального відбивання нами запропоновано метод реконструкції спектрального відбивання на основі змішаного зважування (ЗЗ). Цей метод є поєднанням низки більш ранніх методів, а саме методу оцінки Вінера і так званих методів зважування по групі з меншою різницею кольорів і зважування по групі з меншою похибкою спектрального відбивання. Зокрема, одержано спектральну оцінку з вищою точністю реконструкції шляхом зважування реконстру¬йо¬ваних спектрів, отриманих за різними методами. Ваги цих методів було обрано шляхом мінімізації різниці кольорів. Метод ЗЗ повністю використовує переваги методів, які лежать в його основі. Він володіє високою точністю та нівелює недоліки згаданих методів. Результати наших експериментів підтверджують, що метод ЗЗ підвищує точність реконструкції та стабільність даних для спектрального відбивання.
 
REFERENCES
  1. Xiao K D, Zhu Y, Li C, Connah D, Yates J M and Wuerger S, 2016. Improved method for skin reflectance reconstruction from camera images. Opt. Express. 24: 14934-14950. https://doi.org/10.1364/OE.24.014934
  2. Liang J X, Xiao K D, Pointer M R, Wan X and Li C, 2019. Spectra estimation from raw camera responses based on adaptive local-weighted linear regression. Opt. Express. 27: 5165-5180. https://doi.org/10.1364/OE.27.005165
  3. Li J X, Liu W J, Li T, Rozen I, Zhao J, Bahari B, Kante B and Wang J, 2016. Swimming micro-robot optical nanoscopy. Nano Lett. 16: 6604-6609. https://doi.org/10.1021/acs.nanolett.6b03303
  4. Wang H W, Li J and Chen G X, 2011. Study on key issues of multi-spectral color reproduction technique. Adv. Mater. Res. 117: 93-96. https://doi.org/10.4028/www.scientific.net/AMR.174.93
  5. Zhu Y H, Li B and Xu X Y, 2012. Spectral reconstruction and accuracy appraisal based on pseudo inverse method. IEEE Symposium on Photonics and Optoelectronics, Shanghai, 1-3. https://doi.org/10.1109/SOPO.2012.6270485
  6. Yuasa T, Honma R, Funamizu H, Nishidate I and Aizu Y, 2014. Color adjustment algorithm adapted to the spectral reflectance estimation method. Opt. Rev. 21: 369-372. https://doi.org/10.1007/s10043-014-0056-3
  7. Zhang X D, Wang Q, Yang G F and Wang M, 2014. Acquiring multi-spectral images by digital still cameras based on XYZLMS interim connection space. Chin. Opt. Lett. 12: 113302. https://doi.org/10.3788/COL201412.113302
  8. Zhu Y H, Li B, Chen Q and He S H, 2013. Two spectral reconstruction methods and their accuracy comparison. Appl. Mech. Mater. 239-240: 140-144. https://doi.org/10.4028/www.scientific.net/AMM.239-240.140
  9. Zhang Y and Zhou S S, 2013. Research on reconstruction of spectral reflectance based on principal component analysis. Appl. Mech. Mater. 262: 53-58. https://doi.org/10.4028/www.scientific.net/AMM.262.53
  10. Agahian F, Amirshahi S A and Amirshahi S H, 2008. Reconstruction of reflectance spectra using weighted principal component analysis. Color Res. Appl. 33: 360-371. https://doi.org/10.1002/col.20431
  11. Yamaguchi M, Ohyama N and Murakami Y, 2009. Piecewise Wiener estimation for reconstruction of spectral reflectance image by multipoint spectral measurements. Appl. Opt. 48: 2188-2202. https://doi.org/10.1364/AO.48.002188
  12. Nishidate I, Maeda T, Niizeki K and Aizu Y, 2013. Estimation of melanin and hemoglobin using spectral reflectance images reconstructed from a digital RGB image by the Wiener estimation method. Sensors. 13: 7902-7915. https://doi.org/10.3390/s130607902
  13. Zhang L H, Liang D, Li B, Kang Y, Pan Z, Zhang D and Ma X, 2016. Study on the key technology of spectral reflectivity reconstruction based on sparse prior by a single-pixel detector. Photon. Res. 4: 115-121. https://doi.org/10.1364/PRJ.4.000115
  14. Li B, Zhang H J, Zhang L H, Kang Y, Zhan W, Yi W and Zhang D, 2017. Study on the key technology of spectral reflectance reconstruction based on a single pixel detector. Laser Phys. Lett. 14: 125203. https://doi.org/10.1088/1612-202X/aa8cde
  15. Liang J X and Wan X X, 2017. Optimized method for spectral reflectance reconstruction from camera responses. Opt. Express. 25: 28273-28287. https://doi.org/10.1364/OE.25.028273
  16. Ostu H, Yamamoto M and Hachisuka T, 2018. Reproducing spectral reflectances from tristimulus colours. Comp. Graph. Forum, 37: 370-381. https://doi.org/10.1111/cgf.13332
  17. Cao B, Liao N F and Cheng H B, 2017. Spectral reflectance reconstruction from RGB images based on weighting smaller color difference group. Color Res. Appl. 42: 327-332. https://doi.org/10.1002/col.22091
  18. Zhang X D, Wang Q, Li J C, Zhou X, Yang Y and Xu H, 2016. Estimating spectral reflectance from camera responses based on CIE XYZ tristimulus values under multi-illuminants. Color Res. Appl. 42: 68-77. https://doi.org/10.1002/col.22037
  19. Kong L J, Zeng X, Zhang L H, Zhan W J, Zeng W C, 2019. Research on spectral reflectance reconstruction based on genetic algorithm for selecting multi-illuminants. Spectroscopy and Spectral Analysis. 39: 1162-1168. https://doi.org/10.3964/j.issn.1000-0593(2019)04-1162-07 
  20. Heikkinen V, 2018. Spectral reflectance estimation using Gaussian processes and combination kernels. IEEE Transactions on Image Processing. 27: 3358-3373. https://doi.org/10.1109/TIP.2018.2820839
  21. Koundinya S, Sharma H, Sharma M, Upadhyay A, Manekar R, Mukhopadhyay R, Karmakar A and Chaudhury S, 2018. 2D-3D CNN based architectures for spectral reconstruction from RGB Images. IEEE CVF Conference on Computer Vision and Pattern Recognition Workshop, p. 957-954. https://doi.org/10.1109/CVPRW.2018.00129
  22. Han X H, Shi B X and Zheng Y Q, 2018. Residual HSRCNN: Residual hyper-spectral reconstruction CNN from an RGB image. 24th IEEE Int. Conf. on Patterns Reconstruction, p. 2664-2669. https://doi.org/10.1109/ICPR.2018.8545634
  23. Shimano N and Hironaga M, 2006. Recovery of spectral reflectances of objects being imaged without prior knowledge. IEEE Transactions on Image Processing. 15: 1848-1856. https://doi.org/10.1109/TIP.2006.877069
  24. Zhang L H, Li B, Liang D and Ma X H, 2016. Study on the key technology of spectral reflectance reconstruction based on the weighted measurement matrix. Laser Phys. 26: 075202. https://doi.org/10.1088/1054-660X/26/7/075202
  25. Yoo J H, Kyung W J, Ha H G and Ha Y-H, 2013. Estimation of reflectance based on properties of selective spectrum with adaptive wiener estimation. Proc. SPIE. 8652: 86520D. https://doi.org/10.1117/12.2005444
  26. Wu G, Liu Z and Zhang J, 2015. Spectral color reproduction from CIE tristimulus values using a node address array selection technique. Sign. Proc. Image Proc. & Patt. Recogn. 8: 1786-1790. https://doi.org/10.14257/ijsip.2015.8.9.14
  27. Song J H, Kim C and Yoo Y, 2015. Vein visualization using a smart phone with multispectral Wiener estimation for point-of-care applications. IEEE J. Biomed. Health Inform. 19: 733-788. https://doi.org/10.1109/JBHI.2014.2313145
  28. Zhang L H, Li B and Pan Z L, Liang D, Kang Y, Zhang D and Ma X, 2016. A method for selecting training samples based on camera response. Laser Phys. Lett. 13: 095201. https://doi.org/10.1088/1612-2011/13/9/095201
  29. Cao B, Liao N F, Li Y S and Cheng H, 2016. Improving reflectance reconstruction from tri-stimulus values by adaptively combining colorimetric and reflectance similarities. Opt. Eng. 56: 053104. https://doi.org/10.1117/1.OE.56.5.053104
  30. Niu J, Zhang X D and Wang Q, 2012. Study on the color representing accuracy of spectrum with different wavelength range and interval. Appl. Mech. Mater. 262: 13-17. https://doi.org/10.4028/www.scientific.net/AMM.262.13
(c) Ukrainian Journal of Physical Optics