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.

翻译：商业相机中的白平衡算法通常假设光源为单一均匀光照，当场景存在多个不同色温的光源时会产生不理想的结果。现有针对多光源白平衡的研究通常仅预测像素级光照分布，未能充分理解场景的实际光照条件（包括光源数量与颜色），导致生成缺乏整体一致性的非自然结果。为解决此问题，本文提出一种基于槽注意力的深度白平衡模型，其中每个槽负责表征独立光源。该设计使模型能够生成各光源的色度值与权重图，并通过融合生成最终光照图。此外，我们提出质心匹配损失函数，依据颜色范围调控各槽的激活特性，从而增强模型的光照分离能力。本方法在单光源与多光源白平衡基准测试中均取得最优性能，同时可获取场景光源数量及色度等附加信息。该能力支持光照编辑应用，这是现有方法无法实现的。