Multi-Spectral Image Color Reproduction

From camera to screen, researchers have developed a well-established system for capturing and reproducing the color experience of human eyes. In this study, we aim to upgrade this process by transiting from conventional RGB to multi-spectral image (MSI) color reproduction. While MSI offers evident advantages in color matching, we find out it is not trivial to make good use of more spectral information for color constancy. Therefore, we present a regularized color reproduction system that incorporates a spectral prior-guided optimization strategy to establish a sensor-optimized RGB projection for color matching, along with a learning-based chromatic adaptation model for color constancy. Specifically, we define the RGB projection through an end-to-end optimization under the guidance of sensor spectral sensitivities. Subsequently, we devise a chromatic adaptation neural network that estimates the scene illuminance and an illuminance-adaptive matrix for auto white balancing and dynamic color correction, respectively. Comprehensive experiments show the superiority of our system compared to alternative solutions.