Removal of adherent waterdrops from images acquired with stereo camera

In this paper, we propose a new method that can remove view-disturbing noises from stereo images. One of the thorny problems in outdoor surveillance by a camera is that adherent noises such as waterdrops on the protecting glass surface lens disturb the view from the camera. Therefore, we propose a method for removing adherent noises from stereo images taken with a stereo camera system. Our method is based on the stereo measurement and utilizes disparities between stereo image pair. Positions of noises in images can be detected by comparing disparities measured from stereo images with the distance between the stereo camera system and the glass surface. True disparities of image regions hidden by noises can be estimated from the property that disparities are generally similar with those around noises. Finally, we can remove noises from images by replacing the above regions with textures of corresponding image regions obtained by the disparity referring. Experimental results show the effectiveness of the proposed method.

[1]  Atsushi Yamashita,et al.  Removal of adherent noises from images of dynamic scenes by using a pan-tilt camera , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[2]  Masaaki Yoneda,et al.  Real-time snowfall noise elimination , 1999, Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348).

[3]  Shree K. Nayar,et al.  Vision and the Atmosphere , 2002, International Journal of Computer Vision.

[4]  Olivier Buisson,et al.  Detection and removal of line scratches in motion picture films , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[5]  Stefano Soatto,et al.  Inpainting from multiple views , 2002, Proceedings. First International Symposium on 3D Data Processing Visualization and Transmission.

[6]  Atsushi Yamashita,et al.  A virtual wiper - restoration of deteriorated images by using multiple cameras , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[7]  Anil C. Kokaram,et al.  Detection of missing data in image sequences , 1995, IEEE Trans. Image Process..

[8]  Lisa M. Brown,et al.  A survey of image registration techniques , 1992, CSUR.

[9]  S. Nayar,et al.  Interactive ( De ) Weathering of an Image using Physical Models ∗ , 2003 .

[10]  T. Chan,et al.  Variational image inpainting , 2005 .

[11]  Jean-Michel Morel,et al.  Level lines based disocclusion , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[12]  Yoshiaki Shirai,et al.  Surveillance system based on spatio-temporal information , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[13]  Anil C. Kokaram,et al.  Interpolation of missing data in image sequences , 1995, IEEE Trans. Image Process..

[14]  Guillermo Sapiro,et al.  Image inpainting , 2000, SIGGRAPH.

[15]  Atsushi Yamashita,et al.  A virtual wiper-restoration of deteriorated images by using a pan-tilt camera , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[16]  Shree K. Nayar,et al.  Detection and removal of rain from videos , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..