Abstract
Cameras mounted on vehicles frequently suffer from image shake due to the vehicles’ motions. To remove jitter motions and preserve intentional motions, a hybrid digital image stabilization method is proposed that uses variational mode decomposition (VMD) and relative entropy (RE). In this paper, the global motion vector (GMV) is initially decomposed into several narrow-banded modes by VMD. REs, which exhibit the difference of probability distribution between two modes, are then calculated to identify the intentional and jitter motion modes. Finally, the summation of the jitter motion modes constitutes jitter motions, whereas the subtraction of the resulting sum from the GMV represents the intentional motions. The proposed stabilization method is compared with several known methods, namely, medium filter (MF), Kalman filter (KF), wavelet decomposition (MD) method, empirical mode decomposition (EMD)-based method, and enhanced EMD-based method, to evaluate stabilization performance. Experimental results show that the proposed method outperforms the other stabilization methods.
Highlights
Digital cameras are frequently used to record video information
Serious image shake occurs in complex terrains or under strenuous motions, thereby blurring the video sequences captured by cameras
Recent image stabilization systems can be generally classified into four categories: (1) optical image stabilization systems, which feature a kind of mechanism that stabilizes video sequences by optical computing with high accuracy and speed [6,7]; (2) electronic image stabilization systems, that use accelerometers or motion gyroscopes to detect camera motion and compensate the jitter motion [8]; (3) orthogonal transfer charge-coupled device (CCD) stabilization systems, which use
Summary
Digital cameras are frequently used to record video information. cameras mounted on vehicles frequently suffer from image shaking caused by the vehicles’ motion [1,2]. MF includes a simple mathematical model and is a widely used scheme [13,14] In this method, intentional motion vector is smoothed by averaging GMVs within a window. MF performance highly depends on window size Another traditional method is KF, which estimates intentional motions using a dynamic motion model [15,16,17]. Many empirical mode decomposition (EMD)-based DIS algorithms have been proposed [20,21]. These techniques can adaptively separate jitter and intentional motions from GMV. A hybrid DIS method is proposed that uses variational mode decomposition (VMD) and relative entropy (RE).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
