White balance (WB) correction under multiple illuminants is a persistent challenge in computer vision. Recent methods explored fusion-based approaches, where a neural network linearly blends multiple sRGB versions of an input image, each processed with predefined WB presets. However, we demonstrate that these methods are suboptimal. Additionally, existing fusion-based methods rely on $s R G B$ WB datasets that lack proper multi-illuminant training data. To address these challenges, we introduce two key contributions. First, we propose an efficient transformerbased model that effectively captures spatial dependencies across sRGB WB presets, substantially improving upon linear fusion techniques. Second, we introduce a large-scale multi-illuminant dataset comprising over 16,000 sRGB images rendered with five different WB settings, along with WB-corrected images. Our method achieves up to 100% improvement over existing techniques on our new multiilluminant image fusion dataset. https://revisitingmiwb.github.io