Spectral sensitivity estimation of trichromatic camera based on orthogonal test and window filtering

Abstract

The three-channel spectral sensitivity of a trichromatic camera represents the characteristics of system color space. It is a mapping bridge from the spectral information of a scene to the response value of a camera. In this paper, we propose an estimation method for three-channel spectral sensitivity of a trichromatic camera. It includes calibration experiment by orthogonal test design and the data processing by window filtering. The calibration experiment was first designed by an orthogonal table of the 9-level and 3-factor. A rough estimation model of spectral sensitivity is established on the data pairs of the system input and output in calibration experiments. The data of rough estimation is then modulated by two window filters on frequency and spatial domain. The Luther-Ives condition and the smoothness condition are introduced to design the window, and help to achieve the optimal estimation of the system spectral sensitivity. Finally, the proposed method is verified by some comparison experiments. The results show that the estimated spectral sensitivity is basically consistent with the measured results of the monochromator experiments, the relative full-scale errors of the RGB three-channel is obviously lower than the Wiener filtering method and the Fourier band-limitedness method. The proposed method can estimate the spectral sensitivity of the trichromatic digital camera very well, which is of great significance for the colorimetric characterization and evaluation of imaging systems.

Camera spectral sensitivity estimation based on spectrally tunable LED illumination

“…CB is a matrix of dimension J × L. α is a column vector of dimension L × 1. Now this matrix of basis vector coefficients to be solved can be expressed as Equation ( 7): (10) If the rank of the matrix CB is greater than L, when the number of standard color samples of the target object is greater than the number of basis functions, we can use the pseudo-inverse algorithm to estimate the spectral sensitivity function stably and accurately. In this case, we achieve the estimation of the spectral sensitivity function using fewer number of target object swatches by dimensionality reduction, which can reduce the complexity of the experimental operation and reduce the algorithm running time and cost.…”

“…The weight coefficient during the operation of each LED varies between 0 and 1. (17) Combining the diagonal matrix E of the spectral power distribution of the spectrally tunable LED combination light source, the matrix S of the spectral reflectance of the target object and the matrix B of the basis function with the constraints Equation ( 15) to (17), and substituting into Equation (10), the following objective function can be obtained: (18) Since the spectral power distribution basis function is known, when the values of the weighting coefficients are adjusted, light sources with different spectral power distributions are formed. At this point Equation (18) will get different solutions.…”

Measurement and Estimation of Spectral Sensitivity Functions for Mobile Phone Cameras

“…Typical color samples include reflective color targets, such as color checkers, that are photographed by a camera under known illumination. Fluorescence, LED, and LCD display-based color targets can be used as specialized color samples [ 13 , 14 , 15 ]. The spectral sensitivity functions are estimated from pairs of input and output data comprising the color samples and camera RGB values, respectively.…”