Deterministic Neural Illumination Mapping for Efficient Auto-White Balance Correction

• URL: http://arxiv.org/abs/2308.03939v1
• Date: Mon, 7 Aug 2023 22:44:26 GMT
• Title: Deterministic Neural Illumination Mapping for Efficient Auto-White Balance Correction
• Authors: Furkan K{\i}nl{\i}, Do\u{g}a Y{\i}lmaz, Bar{\i}\c{s} \"Ozcan, Furkan K{\i}ra\c{c}
• Abstract summary: 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.
• Score: 3.441021278275805
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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/

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