Improving reflectance reconstruction from tristimulus values by adaptively combining colorimetric and reflectance similarities

Cao, Bin; Liao, Ningfang; Li, Yasheng; Cheng, Haobo

doi:10.1117/1.oe.56.5.053104

Cited by 10 publications

(22 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are many ways to generate reconstructed spectral distributions from target tristimulus values. [23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41] The solution is not unique; there is an entire "metameric suite" of spectral distributions that share common tristimulus values. The outcome of each reconstruction algorithm differs according to the assumptions and restrictions imposed on the reconstruction.…”

Section: Spectral Reconstruction Computationmentioning

confidence: 99%

“…Spectral reconstruction is the process of generating a distribution (eg, reflectance, power, etc) over a wavelength (or frequency) domain, given only a three‐dimensional representation of the color, such as tristimulus values referenced to some illuminant. There are many ways to generate reconstructed spectral distributions from target tristimulus values . The solution is not unique; there is an entire “metameric suite” of spectral distributions that share common tristimulus values.…”

Section: Spectral Reconstruction Computationmentioning

confidence: 99%

See 1 more Smart Citation

Chromatic adaptation transform by spectral reconstruction

Burns

2019

Color Research & Application

View full text Add to dashboard Cite

A color appearance model (CAM) is an advanced colorimetric tool used to predict color appearance under a wide variety of viewing conditions. A chromatic adaptation transform (CAT) is an integral part of a CAM. Its role is to predict "corresponding colors," that is, a pair of colors that have the same color appearance when viewed under different illuminants, after partial or full adaptation to each illuminant. Modern CATs can sometimes generate colors with negative tristimulus values. For some imaging applications, it is important to maintain positive tristimulus values when applying a CAT. This article proposes a new CAT that does not operate on the standard von Kries model of adaptation. Instead, it uses a spectral reconstruction technique as an intermediate stage in the process, while still requiring only tristimulus values as inputs.It is demonstrated that the proposed CAT will not generate colors outside the spectral locus or colors with negative tristimulus values. The proposed CAT does not use established empirical corresponding-colors data sets to optimize performance, as most modern CATs do, yet it performs as well as or better than the most recent CATs when tested against the data sets of corresponding colors.

show abstract

Section: Spectral Reconstruction Computationmentioning

confidence: 99%

Section: Spectral Reconstruction Computationmentioning

confidence: 99%

Chromatic adaptation transform by spectral reconstruction

Burns

2019

Color Research & Application

View full text Add to dashboard Cite

show abstract

“…Consequently, spectral reflectance estimation from colorimetric data has been the aim of a number of different studies [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ]. The majority of the methods until now have drawn upon linear approaches such as Principal Components Analysis (PCA) which traces back to the reasoning that spectral reflectance of non-fluorescent objects is typically a smooth function of wavelength [ 1 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Spectral Reflectance Reconstruction Using Fuzzy Logic System Training: Color Science Application

Amiri

García-Nieto

Morillas

et al. 2020

Sensors

View full text Add to dashboard Cite

In this work, we address the problem of spectral reflectance recovery from both CIEXYZ and RGB values by means of a machine learning approach within the fuzzy logic framework, which constitutes the first application of fuzzy logic in these tasks. We train a fuzzy logic inference system using the Macbeth ColorChecker DC and we test its performance with a 130 sample target set made out of Artist’s paints. As a result, we obtain a fuzzy logic inference system (FIS) that performs quite accurately. We have studied different parameter settings within the training to achieve a meaningful overfitting-free system. We compare the system performance against previous successful methods and we observe that both spectrally and colorimetrically our approach substantially outperforms these classical methods. In addition, from the FIS trained we extract the fuzzy rules that the system has learned, which provide insightful information about how the RGB/XYZ inputs are related to the outputs. That is to say that, once the system is trained, we extract the codified knowledge used to relate inputs and outputs. Thus, we are able to assign a physical and/or conceptual meaning to its performance that allows not only to understand the procedure applied by the system but also to acquire insight that in turn might lead to further improvements. In particular, we find that both trained systems use four reference spectral curves, with some similarities, that are combined in a non-linear way to predict spectral curves for other inputs. Notice that the possibility of being able to understand the method applied in the trained system is an interesting difference with respect to other ’black box’ machine learning approaches such as the currently fashionable convolutional neural networks in which the downside is the impossibility to understand their ways of procedure. Another contribution of this work is to serve as an example of how, through the construction of a FIS, some knowledge relating inputs and outputs in ground truth datasets can be extracted so that an analogous strategy could be followed for other problems in color and spectral science.

show abstract

“…Recovering the spectral information from colorimetric information has long been of interest for color scientists. [1][2][3][4][5][6] However, the ubiquity of RGB cameras have inspired researchers to start using their response for spectral recovery process. Valero et al showed that using colored filters in front of digital cameras and capturing several different sets of RGB data of a specific object would enhance the spectral recovery accuracy significantly.…”

Section: Introductionmentioning

confidence: 99%

“…Recovering the spectral information from colorimetric information has long been of interest for color scientists . However, the ubiquity of RGB cameras have inspired researchers to start using their response for spectral recovery process.…”

Section: Introductionmentioning

confidence: 99%

A strategy toward spectral and colorimetric color reproduction using ordinary digital cameras

Amiri

Fairchild

2018

Color Research & Application

View full text Add to dashboard Cite

In this work, a methodology is introduced to use ordinary digital RGB cameras for the purpose of spectral and colorimetric color reproduction. First, it is attempted to recover the spectral reflectance from RGB camera response using different approaches, among which, it is shown that weighted nonlinear regression as performed better than other approaches. After analyzing the results, it is realized that although spectrally the results are satisfactory, there is still a significant colorimetric error left, that should be addressed. Therefore, in the second part of the article, different linear and nonlinear matrix transforms are used to change the RGB camera response to CIEXYZ tristimulus values under a specific condition. It is concluded that colorimetric error of the recovery can be reduced significantly when a separate path is used for colorimetric color reproduction. K E Y W O R D S Spectral reflectance recovery, RGB non-standard colorimetric information, CIEXYZ tristimulus values, camera characterization Color Res Appl. 2018;1-10. wileyonlinelibrary.com/journal/col V C 2018 Wiley Periodicals, Inc. | 1

show abstract

Improving reflectance reconstruction from tristimulus values by adaptively combining colorimetric and reflectance similarities

Cited by 10 publications

References 0 publications

Chromatic adaptation transform by spectral reconstruction

Chromatic adaptation transform by spectral reconstruction

Spectral Reflectance Reconstruction Using Fuzzy Logic System Training: Color Science Application

A strategy toward spectral and colorimetric color reproduction using ordinary digital cameras

Contact Info

Product

Resources

About