Computer Science ›› 2022, Vol. 49 ›› Issue (4): 195-202.doi: 10.11896/jsjkx.210300140

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Sketch Colorization Method with Drawing Prior

DOU Zhi, WANG Ning, WANG Shi-jie, WANG Zhi-hui, LI Hao-jie   

  1. College of Software Technology, Dalian University of Technology, Dalian, Liaoning 116000, China
  • Received:2021-03-12 Revised:2021-07-03 Published:2022-04-01
  • About author:DOU Zhi,born in 1996,postgraduate.His main research interests include computer vision and image generation.LI Hao-jie,born in 1972,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include computer vision and image processing.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China(61772108,61932020,61976038).

Abstract: Automatic sketch colorization has become an important research topic in computer vision.Previous methods intent to improve the colorization quality with advanced network architecture or innovative pipeline.However, they usually generate results with concentrated hue, unreasonable saturation and gray distribution.To alleviate these problems, this paper proposes a sketch colorization method with drawing priors.Inspired by the actual coloring process, this method learns the widely used drawing priors (such as hue variation, saturation contrast, and gray contrast) to improve the quality of automatic sketch colorization.Speci-fically, it incorporates pixel-level loss in the HSV color space to gain more natural results with less artifacts.Meanwhile, three heuristic loss functions that introduce the drawing priors such as hue variation, saturation and gray contrast are used to train our method to generate results with harmonious color composition.We compare our method with current state-of-the-art methods on test dataset constructed by real sketch images.Fréchet inception distance (FID) and mean opinion score (MOS) are adopted to measure the similarity between the distribution of real and generated images and the visual quality, respectively.Compared to the second-best method, the experimental results show that the FID of our method decreases by 21.00 and the MOS increases by 0.96, respectively.All the experimental results prove that the proposed method effectively improves the visual quality of automa-tic sketch colorization.

Key words: Automatic sketch colorization, Deep learning, Drawing prior, Generative adversarial networks(GAN), HSV color space

CLC Number: 

  • TP391
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