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| Face Sketch-Photo Synthesis Method Based on Multi-residual Dynamic Fusion Generative Adversarial Networks |
| SUN Rui1,2, SUN Qijing1,2, SHAN Xiaoquan1,2, ZHANG Xudong1 |
1. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230601;
2. Anhui Key Laboratory of Industry Safety and Emergency Technology, Hefei University of Technology, Hefei 230009 |
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Abstract Aiming at the low definition and blurry details in the current face sketch-photo synthesis methods, a face sketch-photo synthesis method based on multi-residual dynamic fusion generative adversarial network is proposed. Firstly, a multi-residual dynamic fusion network is designed. Features are extracted from different dense residual modules and then the residual learning is conducted. Then, the corresponding offsets are generated on the basis of the diverse residual features at different levels. The sampling coordinates of the convolution kernels in different locations are changed according to the offsets. Consequently, the network is focused on important feature adaptively, and geometric detail information and high-level semantic information are integrated effectively without gradual information dropping and redundant information interference. Moreover, a multi-scale perceptual loss is introduced to conduct perceptual comparison on the synthetic images of different resolutions for the regularization of synthetic images from coarse to fine. Experiments on Chinese University of Hong Kong face sketch dataset show that the proposed method produces high-definition images with full detail and consistent color and the synthesized image is closer to real face images.
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Received: 28 September 2021
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| Fund:General Project of National Natural Science Foundation of China(No.6147115461876057), Key Research and Development Plan of Anhui Province(No.202004D07020012) |
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Corresponding Authors:
SUN Rui, Ph.D., professor. His research interests include computer vision and machine learning.
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About author:: SUN Qijing, master student. His research interests include image information processing and computer vision.
SHAN Xiaoquan, master student. His research interests include image information processing and computer vision.
ZHANG Xudong, Ph.D., professor. His research interests include intelligent information processing and machine vision. |
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2022, Vol. 35 
