Attentive Illumination Decomposition Model for Multi-Illuminant White Balancing

Dongyoung Kim, Jinwoo Kim, Junsang Yu, Seon Joo Kim; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 25512-25521

White balance (WB) algorithms in many commercial cameras assume single and uniform illumination leading to undesirable results when multiple lighting sources with different chromaticities exist in the scene. Prior research on multi-illuminant WB typically predicts illumination at the pixel level without fully grasping the scene's actual lighting conditions including the number and color of light sources. This often results in unnatural outcomes lacking in overall consistency. To handle this problem we present a deep white balancing model that leverages the slot attention where each slot is in charge of representing individual illuminants. This design enables the model to generate chromaticities and weight maps for individual illuminants which are then fused to compose the final illumination map. Furthermore we propose the centroid-matching loss which regulates the activation of each slot based on the color range thereby enhancing the model to separate illumination more effectively. Our method achieves the state-of-the-art performance on both single- and multi-illuminant WB benchmarks and also offers additional information such as the number of illuminants in the scene and their chromaticity. This capability allows for illumination editing an application not feasible with prior methods.