Image colorization using generative adversarial network

Erxi Cheng; Ruiqian Ma; Ruichen Qi; Yupeng Tang

doi:10.1117/12.2641206

19 October 2022 Image colorization using generative adversarial network

Erxi Cheng, Ruiqian Ma, Ruichen Qi, Yupeng Tang

Proceedings Volume 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering; 122943Z (2022) https://doi.org/10.1117/12.2641206
Event: 7th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2022), 2022, Xishuangbanna, China

Abstract

In this paper, we present an automatic colorization model for gray scale images based on a generative adversarial networks (GAN). The proposed model is composed of U-Net structure generator and a discriminator with multi-layer convolutional neural network. Then the loss function and weights of true reliability value in the discriminator are optimized. Finally, the corresponding constructed gray scale images colored and colored images are used for training to get an automatic coloring model. In experiments section, we provide various visualization results to validate the effectiveness of our method. The result of a large number of gray scale images coloring experiments show that the model has good learning ability on color transfer and can achieve better coloring effects.

Citation Download Citation

Erxi Cheng, Ruiqian Ma, Ruichen Qi, and Yupeng Tang "Image colorization using generative adversarial network", Proc. SPIE 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering, 122943Z (19 October 2022); https://doi.org/10.1117/12.2641206

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KEYWORDS

RGB color model

Image processing

Gallium nitride

Ultraviolet radiation

Convolutional neural networks

Visual process modeling

Visualization

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