  Auto-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. Source code is
available at https://github.com/birdortyedi/DeNIM/
