Browsing by Author "Zhang, Zhuming"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Deep Line Drawing Vectorization via Line Subdivision and Topology Reconstruction
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Guo, Yi; Zhang, Zhuming; Han, Chu; Hu, Wenbo; Li, Chengze; Wong, Tien-Tsin; Lee, Jehee and Theobalt, Christian and Wetzstein, Gordon
    Vectorizing line drawing is necessary for the digital workflows of 2D animation and engineering design. But it is challenging due to the ambiguity of topology, especially at junctions. Existing vectorization methods either suffer from low accuracy or cannot deal with high-resolution images. To deal with a variety of challenging containing different kinds of complex junctions, we propose a two-phase line drawing vectorization method that analyzes the global and local topology. In the first phase, we subdivide the lines into partial curves, and in the second phase, we reconstruct the topology at junctions. With the overall topology estimated in the two phases, we can trace and vectorize the curves. To qualitatively and quantitatively evaluate our method and compare it with the existing methods, we conduct extensive experiments on not only existing datasets but also our newly synthesized dataset which contains different types of complex and ambiguous junctions. Experimental statistics show that our method greatly outperforms existing methods in terms of computational speed and achieves visually better topology reconstruction accuracy.
  • Thumbnail Image
    Item
    Example‐Based Colourization Via Dense Encoding Pyramids
    (© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2020) Xiao, Chufeng; Han, Chu; Zhang, Zhuming; Qin, Jing; Wong, Tien‐Tsin; Han, Guoqiang; He, Shengfeng; Benes, Bedrich and Hauser, Helwig
    We propose a novel deep example‐based image colourization method called dense encoding pyramid network. In our study, we define the colourization as a multinomial classification problem. Given a greyscale image and a reference image, the proposed network leverages large‐scale data and then predicts colours by analysing the colour distribution of the reference image. We design the network as a pyramid structure in order to exploit the inherent multi‐scale, pyramidal hierarchy of colour representations. Between two adjacent levels, we propose a hierarchical decoder–encoder filter to pass the colour distributions from the lower level to higher level in order to take both semantic information and fine details into account during the colourization process. Within the network, a novel parallel residual dense block is proposed to effectively extract the local–global context of the colour representations by widening the network. Several experiments, as well as a user study, are conducted to evaluate the performance of our network against state‐of‐the‐art colourization methods. Experimental results show that our network is able to generate colourful, semantically correct and visually pleasant colour images. In addition, unlike fully automatic colourization that produces fixed colour images, the reference image of our network is flexible; both natural images and simple colour palettes can be used to guide the colourization.