Browsing by Author "Xie, Haoran"

Now showing items 1-4 of 4

    • Contrastive Semantic-Guided Image Smoothing Network 

      Wang, Jie; Wang, Yongzhen; Feng, Yidan; Gong, Lina; Yan, Xuefeng; Xie, Haoran; Wang, Fu Lee; Wei, Mingqiang (The Eurographics Association and John Wiley & Sons Ltd., 2022)
      Image smoothing is a fundamental low-level vision task that aims to preserve salient structures of an image while removing insignificant details. Deep learning has been explored in image smoothing to deal with the complex ...

    • GlassNet: Label Decoupling‐based Three‐stream Neural Network for Robust Image Glass Detection 

      Zheng, Chengyu; Shi, Ding; Yan, Xuefeng; Liang, Dong; Wei, Mingqiang; Yang, Xin; Guo, Yanwen; Xie, Haoran (© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2022)
      Most of the existing object detection methods generate poor glass detection results, due to the fact that the transparent glass shares the same appearance with arbitrary objects behind it in an image. Different from ...

    • Semi-MoreGAN: Semi-supervised Generative Adversarial Network for Mixture of Rain Removal 

      Shen, Yiyang; Wang, Yongzhen; Wei, Mingqiang; Chen, Honghua; Xie, Haoran; Cheng, Gary; Wang, Fu Lee (The Eurographics Association and John Wiley & Sons Ltd., 2022)
      Real-world rain is a mixture of rain streaks and rainy haze. However, current efforts formulate image rain streaks removal and rainy haze removal as separated models, worsening the loss of image details. This paper attempts ...

    • TogetherNet: Bridging Image Restoration and Object Detection Together via Dynamic Enhancement Learning 

      Wang, Yongzhen; Yan, Xuefeng; Zhang, Kaiwen; Gong, Lina; Xie, Haoran; Wang, Fu Lee; Wei, Mingqiang (The Eurographics Association and John Wiley & Sons Ltd., 2022)
      Adverse weather conditions such as haze, rain, and snow often impair the quality of captured images, causing detection networks trained on normal images to generalize poorly in these scenarios. In this paper, we raise an ...