Video and image enhancement technology is the one of the most important research topics of pattern recognition, image processing, and computer vision. It is broadly applied in many industry fields, such as video monitoring and controlling, intelligent traffic system, military affairs and medical treatment to enhance the stability and high quality of input video and image. Video and image enhancement is theoretically meaningful and has great importance of application. Bad weather condition and hardware defect of capturing device will result in blurry videos and images, with detail information lost. The concept of relative depth information is presented, with which the halos will be restrained effectively. And then we propose efficient edge intensity and defogging ratio to remove fog component adaptively. Experimental results demonstrate that valid edge intensity will be strengthened after defogging. And defogging ratio has correlative extent of descending, meaning the improvement of image quality. When estimating different defogging methods, evaluations are most likely based on subjective observations. Methods based on observations may fail if we have to estimate a large database of images. Detail performance and color performance are the two primary indexes. Valid edge intensity will be combined to compute the detail restoration ratio. Color intensity and color restoration ratio will be proposed base on CIELAB color space. Experimental results show that detail restoration ratio and color restoration ratio have good consistency with subjective visual judgments. Videos with serious shaking have bad visual effects and induce the failure of video processing like motion detection, which has strict demand of video stability. We notice most shaking component of neighboring frames can be decomposed to vertical and horizontal movements. And the rotation and scaling parameters are too little to ignore. Based on this finding, we propose multi-scale global texture registration method to speed up registration between frames. Additionally, we bring up the concept of shared edge consistency to rectify the registration results. Our methods can process real time and output video with good continuity and coherence.
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