Reconstruction of reflectance data by modification of Berns' Gaussian method

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.

Section: Spectral Reconstruction Computationmentioning

confidence: 99%

Section: Spectral Reconstruction Computationmentioning

confidence: 99%

Chromatic adaptation transform by spectral reconstruction

Burns

2019

“…The goal of generating a reflectance distribution over the visible portion of the spectrum from a specified three‐dimensional color specifier (eg, tristimulus values) has been the subject of many investigations . The solution is not unique; there is an entire “metameric suite” of spectral reflectance functions that share a common triplet of tristimulus values for a particular illuminant .…”

Section: Introductionmentioning

confidence: 99%

Numerical methods for smoothest reflectance reconstruction

Burns

2019

Three numerical methods are presented for finding the smoothest reflectance curve associated with a given triplet of tristimulus values. The methods differ in how “smooth” is defined, and also differ in the domain of colors over which they are applicable. The first method is very quick and applies to any tristimulus values, but sometimes can yield reflectance curves with portions that fall outside the range 0 to 1. The second method applies to colors within the spectral locus (real colors) and guarantees that the reflectances produced are positive. The third method applies to colors within the object color solid (object colors) and guarantees that the reflectances fall within the range 0 to 1. The methods are shown to create reflectances that closely resemble those of real colors (natural and synthetic). Focus is given to implementing the numerical methods in very short MATLAB/Octave functions and to understanding the numerical behavior of the methods near the limits of their respective domains of applicability in terms of matrix conditioning and discretization artifacts.

“…The transformation from spectral reflectance to colorimetric coordinates is many‐to‐one and therefore colorimetric data for objects cannot be unambiguously transformed back to spectral reflectance . Thus, different methods such as simulated annealing, simplex method, neural networks, application of subtractive and additive Gaussian primaries, linear methods, the Hawkyard method, the improved Hawkyard method, genetic algorithms, matrix R method, Principal Component Analysis (PCA), nonnegative matrix factorization, Independent Component Analysis, the lookup‐table method, and local linear interpolation method have been developed to extraction the reflectance spectrum from their corresponding colorimetric data …”

Section: Introductionmentioning

confidence: 99%

“…11 Thus, different methods such as simulated annealing, simplex method, neural networks, application of subtractive and additive Gaussian primaries, linear methods, the Hawkyard method, the improved Hawkyard method, genetic algorithms, matrix R method, Principal Component Analysis (PCA), nonnegative matrix factorization, Independent Component Analysis, the lookup-table method, and local linear interpolation method have been developed to extraction the reflectance spectrum from their corresponding colorimetric data. 1,2,5,8,10,[12][13][14][15][16][17][18][19][20][21][22][23] Among these methods, PCA is simple, linear, and nonparametric method and has been widely used to estimation of spectra. 8,[24][25][26][27] PCA is an important mathematical technique in color science, which has been used in data analysis, compression, and extraction of the data-based covariance matrix.…”

Section: Introductionmentioning

confidence: 99%

Effect of classification by competitive neural network on reconstruction of reflectance spectra using principal component analysis

Hajipour

Shams‐Nateri

2016

The best way to describe a color is to study its reflectance spectrum, which provide the most useful information. Different methods were purposed for reflectance spectra reconstruction from CIE tristimulus values such as principal components analysis. In this study, the training samples were first divided into 3, 6, 9, and 12 subgroups by creating a competitive neural network. To do that, L*a*b*, L*C*h or L*a*b*C*h were introduced to neural network as input elements. In order to investigate the performance of reflectance spectra reconstruction, the color difference and RMS between actual and reconstructed data were obtained. The reconstruction of reflectance spectra were improved by using a six or nineneuron layer with L*a*b* input elements.