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Traditional methods, such as the Gray-World assumption, rely on global statistics and struggle in real-world, non-uniform lighting scenarios. Modern deep learning approaches, including convolutional and attention-based architectures, have significantly advanced WB correction but often fail to explicitly account for higher-order feature distribution statistics, which may limit their robustness in challenging environments. This study introduces a novel framework that leverages Exact Feature Distribution Matching (EFDM) as a loss objective to align feature distributions across multiple moments, including mean, variance, skewness, and kurtosis. By modeling lighting as a style factor, the method explicitly addresses distributional shifts caused by complex illumination, offering a robust solution for WB correction. The framework integrates EFDM with a Vision Transformer architecture, enabling precise handling of global and local lighting variations. Extensive experiments on the large-scale multi-illuminant (LSMI) dataset demonstrate the superiority of the proposed approach over state-of-the-art methods and commonly used loss functions when applied to the same architecture. Qualitative and quantitative evaluations highlight its effectiveness in achieving perceptually accurate WB correction, particularly in multi-illuminant environments. By bridging statistical modeling with modern deep learning, this work establishes the critical role of feature distribution alignment in advancing WB correction and sets a new benchmark for robustness and generalization in complex lighting scenarios.<\/jats:p>","DOI":"10.1007\/s00138-025-01680-1","type":"journal-article","created":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T21:15:21Z","timestamp":1742418921000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Feature distribution statistics as a loss objective for robust white balance correction"],"prefix":"10.1007","volume":"36","author":[{"given":"Furkan","family":"K\u0131nl\u0131","sequence":"first","affiliation":[]},{"given":"Furkan","family":"K\u0131ra\u00e7","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,3,19]]},"reference":[{"issue":"1","key":"1680_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/0016-0032(80)90058-7","volume":"310","author":"G Buchsbaum","year":"1980","unstructured":"Buchsbaum, G.: A spatial processor model for object colour perception. 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