Discovering an Image-Adaptive Coordinate System for Photography Processing


Ziteng Cui (The University of Tokyo), Lin Gu (RIKEN), Tatsuya Harada (RIKEN)
The 35th British Machine Vision Conference
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Abstract

Curve & Lookup Table (LUT) based methods directly map a pixel to the target output, making them highly efficient tools for real-time photography processing. However, due to extreme memory complexity to learn full RGB space mapping, existing methods either sample a discretized 3D lattice to build a 3D LUT or decompose into three separate curves (1D LUTs) on the RGB channels. Here, we propose a novel algorithm, IAC, to learn an image-adaptive Cartesian coordinate system in the RGB color space before performing curve operations. This end-to-end trainable approach enables us to efficiently adjust images with a jointly learned image-adaptive coordinate system and curves. Experimental results demonstrate that this simple strategy achieves state-of-the-art (SOTA) performance in various photography processing tasks, including photo retouching, exposure correction, and white-balance editing, while also maintaining a lightweight design and fast inference speed.

Citation

@inproceedings{Cui_2024_BMVC,
author    = {Ziteng Cui and Lin Gu and Tatsuya Harada},
title     = {Discovering an Image-Adaptive Coordinate System for Photography Processing},
booktitle = {35th British Machine Vision Conference 2024, {BMVC} 2024, Glasgow, UK, November 25-28, 2024},
publisher = {BMVA},
year      = {2024},
url       = {https://papers.bmvc2024.org/0307.pdf}
}


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