Image sequence stabilization by lowpass filtering of interframe motion An image sequence stabilization system that removes translational jitter while preserving intentional camera pan is presented. The video sequence is processed to acquire global camera translations from frame to frame (global interframe motion vectors) by motion estimation. The resulting motion vectors are accumulated to construct an absolute frame position vs. frame number signal. This signal is lowpass filtered to remove high frequency components caused by jitter, and retain low frequency parts representing the intentional camera pan. Correction vectors for image frames are obtained by subtracting the absolute frame position from the lowpass filtered value, and stabilization is achieved by the corresponding translation of image frames.

Deep Iterative Frame Interpolation for Fullframe Video Stabilization. Video stabilization is a fundamental and important technique for higher quality videos. Prior works have extensively explored video stabilization, but most of them involve cropping of the frame boundaries and introduce moderate levels of distortion. We present a novel deep approach to video stabilization which can generate video frames without cropping and low distortion. The proposed framework utilizes frame interpolation techniques to generate in between frames, leading to reduced interframe jitter. Once applied in an iterative fashion, the stabilization effect becomes stronger. A major advantage is that our framework is endtoend trainable in an unsupervised manner. In addition, our method is able to run in near realtime (15 fps). To the best of our knowledge, this is the first work to propose an unsupervised deep approach to fullframe video stabilization. We show the advantages of our method through quantitative and qualitative evaluations comparing to the stateoftheart methods.