Journal
VISUAL COMPUTER
Volume 36, Issue 2, Pages 317-331Publisher
SPRINGER
DOI: 10.1007/s00371-018-1609-4
Keywords
Photographic style transfer; Deep learning; Photorealism preservation; Image processing
Categories
Funding
- European Commission under FP7 MarieCurie IRSES project AniNex [612627]
- China Scholarship Council
- Visual Media Research Centre at Tsinghua University
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Image style transfer has attracted much attention in recent years. However, results produced by existing works still have lots of distortions. This paper investigates the CNN-based artistic style transfer work specifically and finds out the key reasons for distortion coming from twofold: the loss of spatial structures of content image during content-preserving process and unexpected geometric matching introduced by style transformation process. To tackle this problem, this paper proposes a novel approach consisting of a dual-stream deep convolution network as the loss network and edge-preserving filters as the style fusion model. Our key contribution is the introduction of an additional similarity loss function that constrains both the detail reconstruction and style transfer procedures. The qualitative evaluation shows that our approach successfully suppresses the distortions as well as obtains faithful stylized results compared to state-of-the-art methods.
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