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

Abstract

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

Reconstruction of spectral reflectance based on mixed weighting and local optimization

“…We have reproduced the WSREG method basing on the experimental procedures typical for the WSCG method [17]. The main regularities, which are observed for the E  , RMSE and GFC parameters with changing number of training samples (in the interval [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20], are shown in Fig. 5.…”

“…A spectral reflectance is one of the most comprehensive and accurate ways to characterize the colour information of object surfaces. Technologies of spectral-reflectance reconstruction have many applications in such fields as skin-colour recognition [1], colour printing, textiles [2], and cell imaging [3]. Moreover, the above technologies can reduce a known phenomenon of metamerism [4][5][6][7].…”

Spectral Reflectance Recovery from Tristimulus Values under Multi-Illuminants

“…Hence, the standard pseudoinverse method leads to large calculation error between the actual and estimated spectra and generates an imprecise result. Obviously, the more similar the spectral reflectance between the testing samples and the training samples is, the more accurate the results can be generated due to the more linearly relation [16,17]. eoretically speaking, if the matrix (A T ) + can be formed by the testing sample's own characteristics, the performance of the recovered spectra would be calculated optimally.…”

Optimization of Maxillofacial Prosthesis

“…Step 4: Color quality evaluation To evaluate color quality of facial prostheses, the average CIELAB color difference (ΔEab) under several standard CIE illuminants needs to calculated. To test spectral reproduction, the root-mean-square error (RMSE) and goodness-of-fit coefficient (GFC) needs to apply [48].…”