Layout Adjustable Simulated Generation Method for Chinese Landscape Paintings Based on CGAN
GU Yang1,2, CHEN Zhaojiong1,2, CHEN Can1,2, YE Dongyi1,2
1.College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116 2.Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou University, Fuzhou 350116
Abstract Creating a complete landscape painting via computer simulation is difficult without studying from global layout viewpoint. To address this issue, a layout-guided Chinese landscape painting simulation method for a complete painting generation is proposed. The characteristics of landscape paintings are taken into account in the design of feasible structures of layout label maps. Composition forms and elements of landscape paintings can be depicted using those structures. On the basis of condition generative adversarial network (CGAN) approach, a multi-scale feature fusion CGAN (MSFF-CGAN) is designed based on layouts and touches of landscape paintings. The proposed network is trained to accomplish heterogeneous transfer from a layout label map to a simulated landscape painting. To deal with rare availability of layout label maps for network training, a color pixel clustering algorithm with semantic correlation is used. In order to enhance the artistic reality of the generated landscape painting, a super resolution network named MemNet is incorporated to refine the texture details. Experimental results show that the proposed method is superior to existing methods in both integrity and artistic reality. Moreover, the proposed method can be used to handle simple graffiti sketches and modify simulated landscape paintings by editing label maps.
Fund:Supported by National Natural Science Foundation of China(No.61672158), Natural Science Foundation of Fujian Province(No.2018J01798)
Corresponding Authors:
CHEN Zhaojiong, master, professor. Her research interests include intelligent image processing and computational intelligence.
About author:: GU Yang, master student. Her research interests include intelligent image processing;CHEN Can, master student. His research interests include image processing;YE Dongyi, Ph.D., professor. His research interests include computational intelligence and data mining.
GU Yang,CHEN Zhaojiong,CHEN Can等. Layout Adjustable Simulated Generation Method for Chinese Landscape Paintings Based on CGAN[J]. , 2019, 32(9): 844-854.
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2019, Vol. 32 
