Grayscale image colorization based on multi-scale input adversarial generative networks

Yibin Lin; Jifeng Sun

doi:10.1117/12.2646838

7 September 2022 Grayscale image colorization based on multi-scale input adversarial generative networks

Yibin Lin, Jifeng Sun

Proceedings Volume 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022); 123291N (2022) https://doi.org/10.1117/12.2646838
Event: Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 2022, Changsha, China

Abstract

Grayscale image colorization is a process of adding reasonable color information to an image, and converting grayscale images into color images is an important and difficult image processing task. The process of colorization is to predict the color information corresponding to the grayscale image by the colorization model. In this paper, the proposed multi-scale input adversarial generative network coloring model with multi-scale input is used in the generator, and the input condition map is fused with the network feature map using the input fusion module so that the network can better utilize the information on each scale of grayscale image for color prediction and improve the effect of colorization. The effectiveness of the generator in this paper is verified experimentally. And it is compared with several existing methods to prove the improvement of the colorization effect of this paper's method.

Citation Download Citation

Yibin Lin and Jifeng Sun "Grayscale image colorization based on multi-scale input adversarial generative networks", Proc. SPIE 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 123291N (7 September 2022); https://doi.org/10.1117/12.2646838

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KEYWORDS

Image processing

RGB color model

Gallium nitride

Image segmentation

Image fusion

Network architectures

Visualization

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