Research On Colorization Algorithm For Night Vision Video Sequence

Natural sense of color night vision can be perceived by the human eye for richer scene information to improve the depth perception of the scene, to improve target identification and detection efficiency, to reduce visual fatigue and to determine the time. Therefore, color night vision technology has become the night vision research focus in the military and civilian aspects that widely used. Compared to static night vision image, dynamic night vision video sequence hide motion information, colorization of the night vision video sequence can be more effective serve military and civilian. This issue mainly research color night vision video sequences of dynamic technology on the basis of color of night vision image, thus expanding the usefulness of color night vision.Firstly, this paper gives a detailed exposition on night vision imaging process, and thus a detailed explanation of night-vision image which different from the normal gray-scale image features. The classic colorization algorithms of night vision image are summarized and discussed.Secondly, while access to the same scene for the infrared(>700nm) and low light (<700nm) images is proposed based on the color lookup table algorithm dual-band color video. First, by the low light and infrared image fusion method, after passing through the color to get outstanding goals and a clear sense of the background details of the natural color images, the establishment of the initial color lookup table, and then use color fix way to get the full color lookup table. After the dual-band video sequence, in the lookup table to find the corresponding color value, fast color-based. Then use of color lookup tables, eliminating the need for space conversion and matching transfer process, the calculation is greatly reduced. And the color values in the color table are obtained by the color transfer, color of the image with a more realistic natural sense.Finally, we proposed a fast method to colorize the vehicle infrared video, using contour feature points to track objects in each frame to get the outline of the regional categories with category colors to transfer color for each region. First, we build the color set of each sample scene, to various types of scenery in a natural color image color characteristics shown, which corresponding to the scene as night vision color; then use of improved and efficient K-Means clustering method to segment infrared key frame, get a better partition and extract the contour feature points; by KLT feature point tracking algorithms to get their positions in the next frame and timely corrected, use B-spline interpolation to recover contours, get the frame outline of each category area. Lastly, colorize each characteristic region by category colors. Experimental results show that this method improves the processing speed and can get more satisfactory effect.