Mobile phone cameras are often significantly more useful than professional digital single-lens reflex (DSLR) cameras. Knowledge of the camera spectral sensitivity function is important in many fields that make use of images. In this study, methods for measuring and estimating spectral sensitivity functions for mobile phone cameras are developed. In the direct measurement method, the spectral sensitivity at each wavelength is measured using monochromatic light. Although accurate, this method is time-consuming and expensive. The indirect estimation method is based on color samples, in which the spectral sensitivities are estimated from the input data of color samples and the corresponding output RGB values from the camera. We first present an imaging system for direct measurements. A variety of mobile phone cameras are measured using the system to create a database of spectral sensitivity functions. The features of the measured spectral sensitivity functions are then studied using principal component analysis (PCA) and the statistical features of the spectral functions extracted. We next describe a normal method to estimate the spectral sensitivity functions using color samples and point out some drawbacks of the method. A method to solve the estimation problem using the spectral features of the sensitivity functions in addition to the color samples is then proposed. The estimation is stable even when only a small number of spectral features are selected. Finally, the results of the experiments to confirm the feasibility of the proposed method are presented. We establish that our method is excellent in terms of both the data volume of color samples required and the estimation accuracy of the spectral sensitivity functions.
We propose an improved method for estimating surface-spectral reflectance from the image data acquired by an RGB digital camera. We suppose a multispectral image acquisition system in the visible range, where a camera captures multiple images for the scene of an object under multiple light sources. First, the observed image data are described using the camera spectral sensitivities, the surface-spectral reflectance, the illuminant spectral power distributions, an additive noise term, and a gain parameter. Then, the optimal reflectance estimate is determined to minimize the mean-square error between the estimate and the original surface-spectral reflectance. We attempt to further improve the estimation accuracy and develop a novel linear estimator in a more general form than the Wiener estimator. Furthermore, we calibrate the imaging system using a reference standard sample. Finally, experiments are performed to validate the proposed method for estimating the surface-spectral reflectance using different mobile phone cameras.
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