White balance estimation (WBE) is one of the most fundamental and crucial steps in modern Image Signal Processor (ISP). Recent years have witnessed the advancements of deep-learning based WBE. However, existing models were mostly trained on individual datasets with limited samples captured using various camera sensors, making it hard for model generalization. In this paper, we propose a novel Channel-Attention optimized U-net model, in which an angular loss is embedded, to accurately estimate the white balance. We demonstrate our approach on recently released largescale dataset “Cube Plus” captured using the same camera sensor, offering state-of-the-art performance.
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