Spectral Color Reproduction from CIE Tristimulus Values Using a Node Address Array Selection Technique

Wu, Guangyuan; Liu, Zhen

doi:10.14257/ijsip.2015.8.9.14

Cited by 5 publications

(10 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The RMSE and the GFC are used to evaluate the spectral-matching degree. The specific RMSE [25][26][27] and GFC [28] parameters are expressed as follows:…”

Section: Accuracy Evaluation For the Spectral-reflectance Reconstructionmentioning

confidence: 99%

Reconstruction of spectral reflectance based on mixed weighting and local optimization

Zhang¹,

Xu²,

Lu³

et al. 2020

Ukr. J. Phys. Opt.

View full text Add to dashboard Cite

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.

show abstract

“…The RMSE and the GFC are used to evaluate the spectral-matching degree. The specific RMSE [25][26][27] and GFC [28] parameters are expressed as follows:…”

Section: Accuracy Evaluation For the Spectral-reflectance Reconstructionmentioning

confidence: 99%

Reconstruction of spectral reflectance based on mixed weighting and local optimization

Zhang¹,

Xu²,

Lu³

et al. 2020

Ukr. J. Phys. Opt.

View full text Add to dashboard Cite

show abstract

“…To illuminate the advantages of the proposed method, the standard pseudoinverse method (method PI) and principal component analysis embedded weight regression technique suggested by Amiri and Amirshahi [8] were compared with the results of the spectral recovery from tristimulus values under multireference illuminants. e root mean square error (RMSE), goodness of fit coefficient (GFC), and CIELAB color difference (∆E ab ) were selected as the evaluation of the spectral recovery accuracy [21], while all the methods of the spectral reflectance recovery perform experimental simulation by the Matlab software.…”

Section: Experiments and Proceduresmentioning

confidence: 99%

Spectral Reflectance Recovery from Tristimulus Values under Multi-Illuminants

Qian

et al. 2019

Journal of Spectroscopy

Self Cite

View full text Add to dashboard Cite

An efficient procedure for recovering spectral reflectance using an object’s tristimulus values under multi-illuminants is proposed by adapting with the characteristics of the testing sample to obtain the transformation matrix of pseudoinverse. Specifically, we propose the reference illuminants selection strategy and local sample weighted strategy to obtain the optimal transformation matrix under multi-illuminants condition. Selecting the reference illuminants are based on the result of the spectral angle mapper (SAM) statistics. The number of the selected local training samples and the weighted local samples can be determined by using the multicolor space Euclidean distance. To compare the experimental results, the proposed method significantly increases the spectral and colorimetric accuracy for the spectral reflectance recovery process.

show abstract

“…In the simulation experiment of this paper, the CIE1976 color difference formula [12][13][14] is used to evaluate the results of the reconstructions. We also calculate and compare the color differences under different light sources such as D50, D65, and standard C light sources.…”

Section: Comparison Of Color Differencesmentioning

confidence: 99%

A method for selecting training samples based on camera response

Zhang

Pan

et al. 2016

Laser Phys. Lett.

View full text Add to dashboard Cite

In the process of spectral reflectance reconstruction, sample selection plays an important role in the accuracy of the constructed model and in reconstruction effects. In this paper, a method for training sample selection based on camera response is proposed. It has been proved that the camera response value has a close correlation with the spectral reflectance. Consequently, in this paper we adopt the technique of drawing a sphere in camera response value space to select the training samples which have a higher correlation with the test samples. In addition, the Wiener estimation method is used to reconstruct the spectral reflectance. Finally, we find that the method of sample selection based on camera response value has the smallest color difference and root mean square error after reconstruction compared to the method using the full set of Munsell color charts, the Mohammadi training sample selection method, and the stratified sampling method. Moreover, the goodness of fit coefficient of this method is also the highest among the four sample selection methods. Taking all the factors mentioned above into consideration, the method of training sample selection based on camera response value enhances the reconstruction accuracy from both the colorimetric and spectral perspectives.

show abstract

Spectral Color Reproduction from CIE Tristimulus Values Using a Node Address Array Selection Technique

Abstract: Abstract

Cited by 5 publications

References 16 publications

Reconstruction of spectral reflectance based on mixed weighting and local optimization

Reconstruction of spectral reflectance based on mixed weighting and local optimization

Spectral Reflectance Recovery from Tristimulus Values under Multi-Illuminants

A method for selecting training samples based on camera response

Contact Info

Product

Resources

About