Multi-Block-Attention-based Color Constancy

Color constancy algorithms estimate and remove the effect of the illuminant from a captured image, enabling consistent color perception under varying illumination conditions. Learning-based methods achieve high accuracy but tend to be biased toward the spectral characteristics of training cameras. Traditional algorithms such as white-patch Retinex are computationally efficient and camera-independent, but underperform when scene statistics deviate from their assumptions. Therefore, in this paper, we propose a hybrid approach that combines classical assumptions with learned features. Our model processes images in a block-based manner through two complementary branches. The backbone branch extracts scene features and identify reliable regions for illuminant estimation. Guided by these saliency maps, the statistics branch computes per-block illumination priors using a soft maximum formulation that provides a differentiable approximation to the classical white-patch Retinex. Features from both branches are fused and refined through multi-head self-attention, which enables blocks to exchange information across the image, addressing the aggregation bias common to block-based methods. Experiments on five benchmarks demonstrate that our model achieves state-of-the-art performance with a compact architecture (∼1.1 MB) that requires no camera-specific calibration or metadata.