Abstract:

Digital image stabilization technique plays important roles in video surveillance and object acquisition. Many useful electronic image stabilization algorithms have been studied. A real-time algorithm is proposed based on field image gray projection which enables the regional odd and even field image to be projected into x and y directions and thus to get the regional gray projection curves in x and y directions, respectively. For the odd field image channel, motion parameters can be estimated via iterative minimum absolute difference based on two successive field image regional gray projection curves. Then motion compensations can be obtained after using the Kalman filter method. Finally, the odd field image is adjusted according to the compensations. In the mean time, motion compensation is applied to the even field image channel with the odd field image gray projection curves of the current frame. By minimizing absolute difference between odd and even field image gray projection curves of the current frame, the inter-field motion parameters can be estimated. Therefore, the even field image can be adjusted by combining the inter-field motion parameters and the odd field compensations. Finally, the stabilized image sequence can be obtained by synthesizing the adjusted odd and even field images. Experimental results show that the proposed algorithm can run in real-time and have a good stabilization performance. In addition, image blurring can be improved.