Perceptual-Aware Sketch Simplification Based on Integrated VGG Layers
| Autor: | Wei Qu, Huaidong Zhang, Miao Peiqi, Tien-Tsin Wong, Minshan Xie, Xuemiao Xu, Xueting Liu, Wenpeng Xiao |
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| Rok vydání: | 2021 |
| Předmět: |
business.industry
Computer science Deep learning Feature extraction Pattern recognition Image segmentation computer.file_format Computer Graphics and Computer-Aided Design Sketch Visualization Feature (computer vision) Signal Processing Computer Vision and Pattern Recognition Artificial intelligence Raster graphics business computer Software |
| Zdroj: | IEEE Transactions on Visualization and Computer Graphics. 27:178-189 |
| ISSN: | 2160-9306 1077-2626 |
| DOI: | 10.1109/tvcg.2019.2930512 |
| Popis: | Deep learning has been recently demonstrated as an effective tool for raster-based sketch simplification. Nevertheless, it remains challenging to simplify extremely rough sketches. We found that a simplification network trained with a simple loss, such as pixel loss or discriminator loss, may fail to retain the semantically meaningful details when simplifying a very sketchy and complicated drawing. In this paper, we show that, with a well-designed multi-layer perceptual loss, we are able to obtain aesthetic and neat simplification results preserving semantically important global structures as well as fine details without blurriness and excessive emphasis on local structures. To do so, we design a multi-layer discriminator by fusing all VGG feature layers to differentiate sketches and clean lines. The weights used in layer fusing are automatically learned via an intelligent adjustment mechanism. Furthermore, to evaluate our method, we compare our method to state-of-the-art methods through multiple experiments, including visual comparison and intensive user study. |
| Databáze: | OpenAIRE |
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