Toward non-metameric reflectance recovery by emulating the spectral neighborhood using corresponding color information

Matching target color in polyolefins by estimating pigment concentrations using a four-flux model

In applications of computer color formulation where color stimuli are optically thick (e.g., textiles, coatings, etc.), a simple single-constant or two-constant theory (e.g., Kubelka-Munk model) would suffice. To accurately predict reflectance and transmittance of materials with optical thickness ranging from optically thin to optically thick (e.g., plastics), mathematically complex radiative transfer theories (e.g., many-flux models) have been recommended. A many-flux model can even predict color formulation involving special-effect pigments (e.g., metallic, pearlescent, etc.), but implementation of such models is manyfold complicated. In the current study, applicability of a relatively simple Maheu-Letoulouzan-Gouesbet (MLG) four-flux radiative transfer model to optically varying pigmented polyolefins is thoroughly investigated. First, the MLG model was implemented to determine absorption and scattering coefficients of over 120 pigments where a new mean relative absolute spectral error (MRASE) between measured and calculated spectral reflectance and transmittance of the calibration samples was minimized as an objective function. Second, currently determined absorption and scattering coefficients were further validated by color recipe prediction of 350 historical product colors. Measured and predicted reflectance curves were compared in units of MRASE, CIEDE2000 color difference, metamerism index, root mean square error, and goodness-of-fit coefficient. Moreover, transmission matching was evaluated in units of percent difference between the required and predicted average transmittance. Results showed that with the current implementation of the MLG four-flux model, color recipes of at least 95% of the target colors can be predicted within the acceptability thresholds in units of different error metrics used in the study.

Dataset artificial augmentation with a small number of training samples for reflectance estimation

The accuracy of the spectral reflectance estimation approaches highly depends on the amount, coverage, and representation of valid samples in the training dataset. We present a dataset artificial augmentation approach with a small number of actual training samples by light source spectra tuning. Then, the reflectance estimation process is carried out with our augmented color samples for commonly used datasets (IES, Munsell, Macbeth, Leeds). Finally, the impact of the augmented color sample number is investigated using different augmented color sample numbers. The results show that our proposed approach can artificially augment the color samples from CCSG 140 color samples to 13791 color samples and even more. The reflectance estimation performances with augmented color samples are much higher than with the benchmark CCSG datasets for all tested datasets (IES, Munsell, Macbeth, Leeds, as well as a real-scene hyperspectral reflectance database). It indicates that the proposed dataset augmentation approach is practical for improving the reflectance estimation performances.

Spectral Reflectance Estimation from Camera Responses Using Local Optimal Dataset

A novel method is proposed to estimate surface-spectral reflectance from camera responses using a local optimal reflectance dataset. We adopt a multispectral imaging system that involves an RGB camera capturing multiple images under multiple light sources. A spectral reflectance database is utilized to locally determine the candidates to optimally estimate the spectral reflectance. The proposed estimation method comprises two stages: (1) selecting the local optimal reflectance dataset and (2) determining the best estimate using only the local optimal dataset. In (1), the camera responses are predicted for the respective reflectances in the database, and then the prediction errors are calculated to select the local optimal dataset. In (2), multiple methods are used; in particular, the Wiener and linear minimum mean square error estimators are used to calculate all statistics, based only on the local optimal dataset, and linear and quadratic programming methods are used to solve optimization problems with constraints. Experimental results using different mobile phone cameras show that the estimation accuracy has improved drastically. A much smaller local optimal dataset among spectral reflectance databases is enough to obtain the optimal estimates. The method has potential applications including fields of color science, image science and technology, computer vision, and graphics.