Abstract
An improved spectral reflectance estimation method was developed to transform captured RGB images to spectral reflectance. The novelty of our method is an iteratively reweighted regulated model that combines polynomial expansion signals, which was developed for spectral reflectance estimation, and a cross-polarized imaging system, which is used to eliminate glare and specular highlights. Two RGB images are captured under two illumination conditions. The method was tested using ColorChecker charts. The results demonstrate that the proposed method could make a significant improvement of the accuracy in both spectral and colorimetric: it can achieve 23.8% improved accuracy in mean CIEDE2000 color difference, while it achieves 24.6% improved accuracy in RMS error compared with classic regularized least squares (RLS) method. The proposed method is sufficiently accurate in predicting the spectral properties and their performance within an acceptable range, i.e., typical customer tolerance of less than 3 DE units in the graphic arts industry.
Highlights
One of the ultimate goals of spectral estimation from a camera image is to predict the spectral reflectance data that represent the physical properties of a device-dependent camera signal
We focused on the development of a more accurate spectral estimation method from raw camera responses using the iteratively reweighted regularization regression model proposed in this study
The schemes for finding the solution are as follows: Θ = DTwTwD + κI −1DTwTwR. Solving this estimation equation is equivalent to a weighted least-squares problem: the weight depends upon the residuals, and the residual depends upon the estimated coefficient, so an iterative solution is required
Summary
One of the ultimate goals of spectral estimation from a camera image is to predict the spectral reflectance data that represent the physical properties of a device-dependent camera signal. Hardeberg et al proposed a method using a principal eigenvector technique and pointed out that any spectral reflectance can be expressed as a linear combination of basic functions and a scalar vector and evaluated illuminant estimation models from color to multispectral imaging [7,10]. Shen et al reported that the partial least squares regression (PLS) method could be adopted in constructing a regression model based on the correlation between response value and spectral reflectance [32] All these studies claimed to have achieved good results using different metrics. We focused on the development of a more accurate spectral estimation method from raw camera responses using the iteratively reweighted regularization regression model proposed in this study. The overall performance of both the proposed and the traditional methods is compared in terms of both spectral and colorimetric accuracy
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