Deterministic Neural Illumination Mapping for Efficient Auto-White Balance Correction

TL;DR

This paper introduces a deterministic illumination mapping method for auto-white balance correction that significantly accelerates processing speed while maintaining or improving accuracy, suitable for real-time high-resolution image applications.

Contribution

The proposed approach offers a novel, resolution-agnostic, deep learning-based AWB correction technique that integrates seamlessly with existing networks and achieves at least 35 times faster processing.

Findings

Achieves 35x faster processing than state-of-the-art methods.

Maintains or improves color correction quality on high-resolution images.

Demonstrates effectiveness and efficiency through extensive experiments.

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 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/