Nonlinear Regression Color Correction Method for RGBN Cameras

With the development of multi-spectral imaging techniques, many new multi-spectral imaging devices have been developed in recent years. Red-green-blue and near-infrared (RGBN) cameras are widely used because they capture visible light and near-infrared light simultaneously, but they inevitably introduce color desaturation. Because there is clear multicollinearity among the RGBN channels, the ordinary least squares regression (OLSR) color correction method performs poorly. To solve color bias and multicollinearity, an RGBN camera color correction pipeline is proposed. A large number of nonlinear regression color correction methods that consist of combinations of four regression methods and nine nonlinear transforms are evaluated in this study. The results show that the proposed OLSR-based compound transform color correction method and partial least-squares regression (PLSR) based Gaussian-core transform color correction method yield better color correction results and are more robust. These approaches reduce the multicollinearity of the RGBN camera channels and will be a valuable reference in the development of RGBN imaging applications.