A Color Correction Method of Color Camera Based on LASSO Regression Model

Color correction is the essential safeguard method to keep the image color constancy for a color camera. In traditional color correction methods, the color calibration coefficients are mostly obtained by the polynomial regression model, whose correction accuracy is often insufficient. So a high order polynomial fitting method based on LASSO (Least Absolute Shrinkage and Selection Operator) regression model is proposed, considering that LASSO can compress the polynomial coefficients efficiently to guarantee the model complexity and improve the correction precision as well. The experiments are conducted using D65 standard light source and ColorChecker 24 as the imaging object. The results characterized by CIELAB color difference function show that compared with traditional linear regression and quadratic polynomial regression methods, the images corrected by LASSO method has a better effect, with the mean color difference value about 5.