• DocumentCode
    2473420
  • Title

    Detection and removal of adherent noises in video from a moving camera

  • Author

    Wang, Huanyu ; Tan, Zhiming ; Higashi, Akihiro

  • Author_Institution
    Fujitsu R&D Center Co., Ltd., Shanghai, China
  • fYear
    2012
  • fDate
    14-17 Oct. 2012
  • Firstpage
    2545
  • Lastpage
    2550
  • Abstract
    A novel method to detect and remove adherent noises in videos from a moving camera is presented in this paper. The basic idea is to detect the adherent noises by spatio-temporal image processing technology first and then remove and restore the information in noise regions using a 3D inpainting technology. Under the condition that camera motion is unknown and unconstrained, a 3D spatio-temporal image is acquired by a perspective transformation without motion estimation and the static background of the spatio-temporal image is modeled. Then the adherent noises are extracted by tracing trajectories of the difference between adherent noises and static background and the data is eliminated in regions of adherent noises. Finally, a 3D spatio-temporal exemplar-based texture synthesis approach is applied to inpaint the miss information. The proposed method is shown to be very effective on real video acquired from a moving camera.
  • Keywords
    image denoising; image texture; video signal processing; 3D inpainting technology; adherent noises detection; adherent noises removal; exemplar-based texture synthesis approach; moving camera; spatiotemporal image processing technology; static background; video applications; Cameras; Glass; Image restoration; Lenses; Noise; Robot vision systems; Surveillance; Adherent noise; background subtraction; image denoising; image restoration; moving camera; spatio-temporal image processing; video applications;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems, Man, and Cybernetics (SMC), 2012 IEEE International Conference on
  • Conference_Location
    Seoul
  • Print_ISBN
    978-1-4673-1713-9
  • Electronic_ISBN
    978-1-4673-1712-2
  • Type

    conf

  • DOI
    10.1109/ICSMC.2012.6378128
  • Filename
    6378128