Abstract In the existing image style transferring methods, the primary structure of the target image is often deformed while a distinctive style is transferred. Therefore, a loss function for style transferring based on DCNN is designed. In addition to the original items]two more regularization terms are introduced in the function. To maintain the primary structure of the target image, the edge information extracted by the LoG operator is used as the feature for the primary structure. The first regularization term is constituted by feature difference between the resultant image and the target image. The second regularization term is composed of features obtained by Gabor filter to enhance the description of directional style features since artistic style is closely related to directional characteristics such as strokes, texture orientation and color flow while depth features are more focused on depicting global information. This item avoids the weakening effect of transferred style due to the maintenance of the primary structure. The experimental results show that the proposed method maintains better primary structure of the target image while successfully transferring a distinctive style.
Fund:Supported by National Natural Science Foundation of China(No.61672158), Natural Science Foundation of Fujian Province(No.2018J1798,2016J05155)
Corresponding Authors:
CHEN Zhaojiong, master, professor. Her research interests include intelligent image processing and computational intelligence.
About author:: LIN Xing, master student. His research interests include image processing;YE Dongyi, Ph.D., professor. His research interests include computational intelligence and data mining.
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2018, Vol. 31 
