Subject to the response uniformity of photoelectric sensors, the captured raw images always have serious chroma distortions. How to determine the mapping matrix between RGB and XYZ color spaces is important for the color distortion correction. However, the commonly used algorithms cannot give consideration to the precision and the adaptability. A more reasonable mapping algorithm based on variable-exponent polynomial regression is proposed to evaluate the mapping matrix coefficients. Variable-exponent regularization with the Lρ-norm (1 < ρ < 2) combines the features of lasso regression and ridge regression methods, owning both the sparsity and smoothing properties. The optimal solution for the variable-exponent regularization is given using lagged fix-point iteration method. Data from the standard color correction experiments are used to test the variable-exponent, lasso, ridge, and least-squares regression algorithms with different polynomial regression models. The results demonstrate that the proposed algorithm has the best performance.
Color correction Polynomial regression Regularization Variable-exponent
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