ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
A Closed Form Solution to Natural Image Matting
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
ACM SIGGRAPH 2008 papers
A fast semi-inverse approach to detect and remove the haze from a single image
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Single Image Haze Removal Using Dark Channel Prior
IEEE Transactions on Pattern Analysis and Machine Intelligence
An efficient pattern-less background modeling based on scale invariant local states
AVSS '11 Proceedings of the 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance
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The quality of outdoor surveillance videos are always degraded by bad weathers, such as fog, haze, and snowing. The degraded videos not only provide poor visualizations, but also increase the difficulty of vision-based analysis such as foreground/background segmentation. However, haze/fog removal has never been an easy task, and is often very time consuming. Most of the existing methods only consider a single image, and no temporal information of a video is used. In this paper, a novel adaptive background defogging method is presented. It is observed that most of the background regions between two consecutive video frames do not vary too much. Based on this observation, each video frame is firstly defogged by a background transmission map which is generated adaptively by the proposed foreground decremental preconditioned conjugate gradient (FDPCG). It is shown that foreground/background segmentation can be improved dramatically with such background-defogged video frames. With the help of a foreground map, the defogging of foreground regions is then completed by 1) foreground transmission estimation by fusion, and 2) transmission refinement by the proposed foreground incremental preconditioned conjugate gradient (FIPCG). Experimental results show that the proposed method can effectively improve the visualization quality of surveillance videos under heavy fog and snowing weather. Comparing with the state-of-the-art image defogging methods, the proposed method is much more efficient.