Deterministic Neural Illumination Mapping for Efficient Auto-White Balance Correction

Abstract

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 highresolution 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/

Paper

Bibtex:

@inproceedings{kinli2023deterministic,
  title={Deterministic neural illumination mapping for efficient auto-white balance correction},
  author={K{\i}nl{\i}, Furkan and Y{\i}lmaz, Do{\u{g}}a and {\"O}zcan, Bar{\i}{\c{s}} and K{\i}ra{\c{c}}, Furkan},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={1139--1147},
  year={2023}
}