Author
Kınlı, Osman Furkan, Yılmaz, Doğa, Özcan, Barış, Kıraç, Mustafa Furkan
Publication Date
2023
Publication Place
-
IEEE
Subject
Auto white balance correction, Deterministic color mapping, Reverse style transfer, Style factor
Type
Document
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
979-835030744-3
Record ID
d197508d-61d8-45db-99c4-72689d9b3bcd
Library Location
Computer Science
Date
2023
Sample Text
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.
DOI
10.1109/ICCVW60793.2023.00122
