Digital image stabilization based on adaptive motion filtering with feedback correction

For online digital image stabilization system, the camera usually moves with diverse and variable modes, which make the motion filtering process difficult to reserve the intentional fluctuations and remove unwanted jitters simultaneously. This paper presents an adaptive motion filtering algorithm with feedback correction. Firstly, based on the low frequency character of intentional motion in adjacent frames, the intentional velocity is regarded as the control variable, thus the modified one dimension Kalman filtering algorithm is proved to converge to a balance state of consistency and stabilization. Secondly, according to the mutual restraint of consistency and stabilization, this paper proposes two corresponding online feedback factors to reflect the immediate filtering performances. Hence, a motion filtering algorithm with improved Kalman filtering and parameter self-adjustment is realized, which can describe the real camera motion flexibly, as well as adapt to its changes. At last, an objective evaluation method for motion filtering is presented from the aspects of integral consistency, integral stabilization and integral robustness. Compared with other classical motion filtering algorithms, experimental results indicate that the proposed algorithm is more fast-computing and adaptive for different moving modes of the camera, which can reserve the intentional motions and remove the jitters steadily.