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Mollifying Atmospheric Instability in Video Surveillance System Using DT-CWT

Shraddha Agnihotri, Prof. Anil Bende

Atmospheric instability is a challenging problem in video surveillance. This paper describes a new method for mollifying the effects of atmospheric distortion on observed images, particularly in distorted video turbulence which degrades a region of interest (ROI) .Due to this valuable information from video can be lost, and distorted video is of no use. So to extract important information or data from distorted video use CLEAR algorithm which retrieve information from distorted video. This paper describes a new method for mollifying the effects of atmospheric instability on observed images, particularly airborne turbulence which degrades a region of interest (ROI).In order to provide accurate detail or data from objects behind the distorting layer, a simple and efficient frame selection method is proposed to get informative ROIs from only good-quality frames. We solve the space-variant instability called distortion problem using region-based fusion based on the Dual Tree Complex Wavelet Transform (DT-CWT). We also propose an object alignment method for pre-processing the ROI since this can exhibit significant offsets and distortions between frames. Simple haze removal is used as the final step. To remove the haze from distorted video we use the CLEAR algorithm to extract important information from video. The CLEAR algorithm has series of step to clear video and lastly get restored image. For last step i.e., haze removal dark channel prior method is used. The proposed method performs very well with atmospherically instable or distorted video and outperforms other existing methods