Spectral reflectance reconstruction from RGB images based on weighting smaller color difference group

Cao, Bin; Liao, Ningfang; Cheng, Haobo

doi:10.1002/col.22091

Cited by 36 publications

(38 citation statements)

References 29 publications

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“…where Q denotes the transformation matrix. The accuracy of reflectance reconstruction is determined by the method of solving for the transformation matrix Q [15,19,26]. The widely used pseudo-inverse (PI) algorithm is used in the method proposed in this study [19].…”

Section: Imaging Modelmentioning

confidence: 99%

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Spectra estimation from raw camera responses based on adaptive local-weighted linear regression

et al. 2019

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An improved spectral reflectance estimation method is developed to transform raw camera RGB responses to spectral reflectance. The novelty of our method is to apply a local weighted linear regression model for spectral reflectance estimation and construct the weighting matrix using a Gaussian function in CIELAB uniform color space. The proposed method was tested using both a standard color chart and a set of textile samples, with a digital RGB camera and by ten times ten-fold cross-validation. The results demonstrate that our method gives the best accuracy in estimating both the spectral reflectance and the colorimetric values in comparison with existing methods. IntroductionDigital cameras can be used to provide spectral data for many applications and thus the development of algorithms to calculate these spectral data from image RGB data is of prime importance. Different digital camera-based spectral imaging systems have been developed for practical applications, such as a camera with bandpass filters [1-3], a camera utilizing multiple illuminants [1,4,5], and a camera with three-channel responses (but a single RGB image) under a specific illuminant [6-13]. The latter system, which can be used to estimate the spectral reflectance from a single RGB image, has received considerably more interest because of the low cost of the devices with high resolution and their convenience in practical applications. The inherent problem of image registration which exists in optical bandpass filter-based spectral imaging systems [1][2][3]14] can be overcome by just one exposure using a conventional digital RGB camera. Accurate spectral and colorimetric estimation are both critical for practical applications and, since the digital RGB values are readily available from an image, an algorithm that accurately estimates the spectral reflectance from these RGB camera responses can be very useful.Methods to derive spectral data from camera responses can be divided into two types: model-based and training-based [15]. Since it is complex and expensive to characterize the camera sensitivity functions for the model-based method, the more easily implemented training-based method is both more convenient and more practical. Many training-based spectral estimation methods have been proposed in recent years [5][6][7][8][9][10][11][12][13]16]. Connah [6], Heikkinen [5], and Shen [8] proposed the use of a nonlinear regression method based on a polynomial model for spectral estimation, with consideration being given to the potential over-fitting problem in the polynomial-based regression model. Xiao et al. [10] illustrated that it is effective to combine the polynomial model with the eigenvector space of principal

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Section: Imaging Modelmentioning

confidence: 99%

“…Li [9] and Cao [19] proposed estimation methods based on local linear approximation. In their studies however, the estimation accuracy is limited by the hypothesis of mapping the linearity of tristimulus color space to spectral reflectance space.…”

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confidence: 99%

Spectra estimation from raw camera responses based on adaptive local-weighted linear regression

et al. 2019

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“…There are many techniques available to up‐sample RGB values to a full spectrum. Other communities are interested in matching reference spectra (see for instance [CLC17]). We quickly recapitulate the most relevant methods for graphics:…”

Section: Background and Previous Workmentioning

confidence: 99%

Wide Gamut Spectral Upsampling with Fluorescence

Jung

Wilkie

Hanika³

et al. 2019

Computer Graphics Forum

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Physically based spectral rendering has become increasingly important in recent years. However, asset textures in such systems are usually still drawn or acquired as RGB tristimulus values. While a number of RGB to spectrum upsampling techniques are available, none of them support upsampling of all colours in the full spectral locus, as it is intrinsically bigger than the gamut of physically valid reflectance spectra. But with display technology moving to increasingly wider gamuts, the ability to achieve highly saturated colours becomes an increasingly important feature. Real materials usually exhibit smooth reflectance spectra, while computationally generated spectra become more blocky as they represent increasingly bright and saturated colours. In print media, plastic or textile design, fluorescent dyes are added to extend the boundaries of the gamut of reflectance spectra. We follow the same approach for rendering: we provide a method which, given an input RGB tristimulus value, automatically provides a mixture of a regular, smooth reflectance spectrum plus a fluorescent part. For highly saturated input colours, the combination yields an improved reconstruction compared to what would be possible relying on a reflectance spectrum alone. At the core of our technique is a simple parametric spectral model for reflectance, excitation, and emission that allows for compact storage and is compatible with texture mapping. The model can then be used as a fluorescent diffuse component in an existing more complex BRDF model. We also provide importance sampling routines for practical application in a path tracer.

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“…where, RGBlin_Ln is the matrix that also contains the multiplication terms of RGB for learning samples, and Mnr is the matrix containing the relationship between the spectral and RGB information of learning samples. Therefore, by multiplying the matrix Mnr by the matrix containing the RGB camera response of testing samples that also contains their multiplication the same as RGBlin_Ln, it is possible to obtain their spectral reflectance, shown in Equation 9. The nonlinear regression used is a polynomial with 17 coefficients, which leads to an acceptable result.…”

Section: M5refl3w3pinv Rgbl3wmentioning

confidence: 99%

“…8 Finally, Cao et al devised a method for recovering the spectra from their RGB camera response, in which they weighted sample having smaller color difference from the learning samples more than those having a bigger color difference. 9 They reconstructed the reflectance spectra of the objects using only a few of the learning samples' spectra around that specific sample. Their method will be compared to the methods proposed in this article because it is one of the most recent article published in reflectance recovery from camera response.…”

Section: Introductionmentioning

confidence: 99%

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

Amiri

Fairchild

2018

Color Research & Application

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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

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Spectral reflectance reconstruction from RGB images based on weighting smaller color difference group

Cited by 36 publications

References 29 publications

Spectra estimation from raw camera responses based on adaptive local-weighted linear regression

Spectra estimation from raw camera responses based on adaptive local-weighted linear regression

Wide Gamut Spectral Upsampling with Fluorescence

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

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