A Color Balancing Algorithm for Cameras

Color constancy refers to the ability to recognize the true color of objects in a scene regardless of the illumination which is incident upon them. While the human visual system is color-constant to a large extent, digital cameras have to rely on fast color balancing algorithms, integrated into their image signal processing (ISP) pipeline, in order to estimate the illumination in a scene and correct for it digitally. In this paper, we discuss known methods of applying color balancing in cameras, and argue that separating the correction stage into two parts diagonal correction before the demosaicing stage and a linear correction afterwards, can result in improved image quality. We continue to analyze the color gamut of natural illumination using a large database of natural images, coupled with ground truth illumination information, and derive a new, fast color balancing algorithm for digital cameras. Contrary to existing methods, our method uses only part of the scene gamut which may correspond to neutral gray color to estimate the illumination. We compare our method against six known fast color balancing algorithms on the database. Our experiments show that the proposed method outperforms these algorithms, while maintaining similar computational complexity.