Paper
14 March 2024 Data-driven gradient mapping adjustment method for color optimization in digital illustrations
Hanna Xie, Siyi Chen, Jie Li
Author Affiliations +
Proceedings Volume 13074, Fifth International Conference on Image, Video Processing, and Artificial Intelligence (IVPAI 2023); 130740B (2024) https://doi.org/10.1117/12.3023736
Event: Fifth International Conference on Image, Video Processing and Artificial Intelligence, 2023, Shenzhen, China
Abstract
In recent years, gradient mapping based on colormaps has gained popularity in image editing and digital painting. However, this mapping often results in the loss of detailed information in the image. In response to this challenge, we propose a method aimed at revealing hidden details without deviating from the original color tendencies of the gradient colormaps. To achieve this goal, we employ a non-linear gradient mapping combined with a generative adversarial network to iteratively adjust the colormap parameters guided by changes in the color space of the image. Through a series of experiments, we demonstrate that our method not only fulfills the desired objectives but also exhibits outstanding performance in color representation.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Hanna Xie, Siyi Chen, and Jie Li "Data-driven gradient mapping adjustment method for color optimization in digital illustrations", Proc. SPIE 13074, Fifth International Conference on Image, Video Processing, and Artificial Intelligence (IVPAI 2023), 130740B (14 March 2024); https://doi.org/10.1117/12.3023736
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KEYWORDS
Image processing

Image quality

Associative arrays

Gallium nitride

Data modeling

Ablation

Design

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