Kınlı, Osman FurkanYılmaz, DoğaÖzcan, BarışKıraç, Mustafa Furkan2024-02-022024-02-022023979-835030744-3http://hdl.handle.net/10679/9119https://doi.org/10.1109/ICCVW60793.2023.00122Auto-white balance (AWB) correction is a critical operation in image signal processors for accurate and consistent color correction across various illumination scenarios. This paper presents a novel and efficient AWB correction method that achieves at least 35 times faster processing with equivalent or superior performance on high-resolution images for the current state-of-the-art methods. Inspired by deterministic color style transfer, our approach introduces deterministic illumination color mapping, leveraging learnable projection matrices for both canonical illumination form and AWB-corrected output. It involves feeding high-resolution images and corresponding latent representations into a mapping module to derive a canonical form, followed by another mapping module that maps the pixel values to those for the corrected version. This strategy is designed as resolution-agnostic and also enables seamless integration of any pre-trained AWB network as the backbone. Experimental results confirm the effectiveness of our approach, revealing significant performance improvements and reduced time complexity compared to state-of-the-art methods. Our method provides an efficient deep learning-based AWB correction solution, promising real-time, high-quality color correction for digital imaging applications.engrestrictedAccessconferenceObject1131113910.1109/ICCVW60793.2023.00122Auto white balance correctionDeterministic color mappingReverse style transferStyle factor2-s2.0-85182940405