Attentive Illumination Decomposition Model for Multi-Illuminant White
  Balancing
Dongyoung Kim Jinwoo Kim Junsang Yu Seon Joo Kim
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.