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Open AccessArticle

Digital Image Stabilization Method Based on Variational Mode Decomposition and Relative Entropy

by Duo Hao 1, Qiuming Li 2 and Chengwei Li 1,*
1
School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
2
School of Astronautics, Harbin Institute of Technology, Harbin 150001, China
*
Author to whom correspondence should be addressed.
Entropy 2017, 19(11), 623; https://doi.org/10.3390/e19110623
Received: 11 September 2017 / Revised: 15 November 2017 / Accepted: 16 November 2017 / Published: 18 November 2017
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. View Full-Text
Keywords: digital image stabilization; variational mode decomposition; relative entropy; jitter motion; intentional motion digital image stabilization; variational mode decomposition; relative entropy; jitter motion; intentional motion
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Hao, D.; Li, Q.; Li, C. Digital Image Stabilization Method Based on Variational Mode Decomposition and Relative Entropy. Entropy 2017, 19, 623.

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