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Human Face Sketch to RGB Image with Edge Optimization and Generative Adversarial Networks

Feng Zhang1, Huihuang Zhao1,2,*, Wang Ying1,2, Qingyun Liu1,2, Alex Noel Joseph Raj3, Bin Fu4

1 College of Computer Science and Technology, Hengyang Normal University, Hengyang, 421002, China
2 Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang, 421002, China
3 Key Laboratory of Digital Signal and Image Processing of Guangdong, Shantou, 515063, China
4 Department of Computer Science, University of Texas Rio Grande Valley, Edinburg, TX, USA

* Corresponding Author: Huihuang Zhao. Email: email

Intelligent Automation & Soft Computing 2020, 26(6), 1391-1401.


Generating an RGB image from a sketch is a challenging and interesting topic. This paper proposes a method to transform a face sketch into a color image based on generation confrontation network and edge optimization. A neural network model based on Generative Adversarial Networks for transferring sketch to RGB image is designed. The face sketch and its RGB image is taken as the training data set. The human face sketch is transformed into an RGB image by the training method of generative adversarial networks confrontation. Aiming to generate a better result especially in edge, an improved loss function based on edge optimization is proposed. The experimental results show that the clarity of the output image, the maintenance of facial features, and the color processing of the image are enhanced best by the image translation model based on the generative adversarial network. Finally, the results are compared with other existing methods. Analyzing the experimental results shows that the color face image generated by our method is closer to the target image, and has achieved a better performance in term of Structural Similarity (SSIM).


Cite This Article

F. Zhang, H. Zhao, W. Ying, Q. Liu, A. Noel Joseph Raj et al., "Human face sketch to rgb image with edge optimization and generative adversarial networks," Intelligent Automation & Soft Computing, vol. 26, no.6, pp. 1391–1401, 2020.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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