Auxiliary Reference Samples for Extrapolating Spectral Reflectance from Camera RGB Signals

Surface spectral reflectance is useful for color reproduction. In this study, the reconstruction of spectral reflectance using a conventional camera was investigated. The spectrum reconstruction error could be reduced by interpolating camera RGB signals, in contrast to methods based on basis spectra, such as principal component analysis (PCA). The disadvantage of the interpolation method is that it cannot interpolate samples outside the convex hull of reference samples in the RGB signal space. An interpolation method utilizing auxiliary reference samples (ARSs) to extrapolate the outside samples is proposed in this paper. The ARSs were created using reference samples and color filters. The convex hull of the reference samples and ARSs was expanded to enclose outside samples for extrapolation. A commercially available camera was taken as an example. The results show that with the proposed method, the extrapolation error was smaller than that of the computationally time-consuming weighted PCA method. A low cost and fast detection speed for spectral reflectance recovery can be achieved using a conventional camera

Irradiance Independent Spectrum Reconstruction from Camera Signals Using the Interpolation Method

The spectrum of light captured by a camera can be reconstructed using the interpolation method. The reconstructed spectrum is a linear combination of the reference spectra, where the weighting coefficients are calculated from the signals of the pixel and the reference samples by interpolation. This method is known as the look-up table (LUT) method. It is irradiance-dependent due to the dependence of the reconstructed spectrum shape on the sample irradiance. Since the irradiance can vary in field applications, an irradiance-independent LUT (II-LUT) method is required to recover spectral reflectance. This paper proposes an II-LUT method to interpolate the spectrum in the normalized signal space. Munsell color chips irradiated with D65 were used as samples. Example cameras are a tricolor camera and a quadcolor camera. Results show that the proposed method can achieve the irradiance independent spectrum reconstruction and computation time saving at the expense of the recovered spectral reflectance error. Considering that the irradiance variation will introduce additional errors, the actual mean error using the II-LUT method might be smaller than that of the ID-LUT method. It is also shown that the proposed method outperformed the weighted principal component analysis method in both accuracy and computation speed

Spectral Reflectance Recovery from the Quadcolor Camera Signals Using the Interpolation and Weighted Principal Component Analysis Methods

The recovery of surface spectral reflectance using the quadcolor camera was numerically studied. Assume that the RGB channels of the quadcolor camera are the same as the Nikon D5100 tricolor camera. The spectral sensitivity of the fourth signal channel was tailored using a color filter. Munsell color chips were used as reflective surfaces. When the interpolation method or the weighted principal component analysis (wPCA) method is used to reconstruct spectra, using the quadcolor camera can effectively reduce the mean spectral error of the test samples compared to using the tricolor camera. Except for computation time, the interpolation method outperforms the wPCA method in spectrum reconstruction. A long-pass optical filter can be applied to the fourth channel for reducing the mean spectral error. A short-pass optical filter can be applied to the fourth channel for reducing the mean color difference, but the mean spectral error will be larger. Due to the small color difference, the quadcolor camera using an optimized short-pass filter may be suitable as an imaging colorimeter. It was found that an empirical design rule to keep the color difference small is to reduce the error in fitting the color-matching functions using the camera spectral sensitivity functions