End-to-end reference image-based coloring

Huiying Jia; Xuesong Su

doi:10.1117/12.3035550

19 July 2024 End-to-end reference image-based coloring

Huiying Jia, Xuesong Su

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 1321308 (2024) https://doi.org/10.1117/12.3035550
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

This study proposes an end-to-end method aimed at achieving intelligent colorization of images. This approach enables automatic colorization of grayscale images by taking in a grayscale image and a colored reference image as input. We designed a deep neural network model to learn the mapping relationship between grayscale images and colored reference images in an end-to-end manner. During training, we utilized a large amount of image data with real color labels and optimized the model using generative adversarial networks. Experimental results demonstrate the superiority of our method in color restoration and detail preservation across different types of images. Compared to traditional methods, the end-to-end approach offers higher levels of automation and faster colorization speed, opening up new possibilities in the field of image colorization.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Huiying Jia and Xuesong Su "End-to-end reference image-based coloring", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 1321308 (19 July 2024); https://doi.org/10.1117/12.3035550

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