Data-Driven Light Source Selection for Camera Colorimetric Calibration

The colorimetric calibration of cameras are critical in imaging systems, with the sources used in light booths being widely used in practice. These sources, however, may not good presentations of the sources in real life, which possibly results in poor colors. In this study, we adopted a genetic algorithm and a large dataset of real light sources to identify an optimal set of sources that can better represent the sources in real life. The experiment results suggested that the identified set of sources can result in better color performance. Moreover, the selection of the sources was much less complicated in comparison to manual selections, which can be considered and implemented in practice.