• DocumentCode
    3081446
  • Title

    Fast single image dehazing through Edge-Guided Interpolated Filter

  • Author

    Ximei Zhu ; Ying Li ; Yu Qiao

  • Author_Institution
    Shenzhen Key Lab. of Comput. Vision & Pattern Recognition, Shenzhen Inst. of Adv. Technol., Shenzhen, China
  • fYear
    2015
  • fDate
    18-22 May 2015
  • Firstpage
    443
  • Lastpage
    446
  • Abstract
    Images and videos taken in foggy weather often suffer from low visibility. Recent studies demonstrate the effectiveness of dark channel prior [3] and guided filter [4] based approaches for image dehazing. However, these methods require high computational cost which makes them infeasible for realtime and embedding systems. In this paper, we propose Edge-Guided Interpolated Filter (EGIF) for fast image and video dehazing. The main contributions are twofold. Firstly, we develop Guided Interpolated Filter (GIF) to significantly speed up the estimation of transmission map, which is the most computational cost step in previous methods. Secondly, we utilize edge map as guidance image in GIF to enhance the fine details in dehazed images. Experimental results show that GIF can largely improve the computational efficiency and achieve comparable dehazing performance as previous guided filter based methods. EGIF can further enhance the sharpness of transmission map. Our method can achieve real-time processing for image of size 1024 × 768 with single CPU core (2GHz).
  • Keywords
    image filtering; image restoration; interpolation; real-time systems; computational cost; computational efficiency; dark channel; edge-guided interpolated filter; frequency 2 GHz; guidance image; real-time processing; single CPU core; single image dehazing; transmission map; video dehazing; Atmosphere; Computer vision; Image edge detection; Image restoration; Interpolation; Real-time systems; Videos;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Vision Applications (MVA), 2015 14th IAPR International Conference on
  • Conference_Location
    Tokyo
  • Type

    conf

  • DOI
    10.1109/MVA.2015.7153106
  • Filename
    7153106