Fast robust digital image stabilization based on feature matching

In view of the problems that the translation and small angle rotation motion always exist between adjacent frames when the handheld mobile camera is filming video sequence, and it is easily affected by noise and illumination changes,we put forward a kind of real-time robust digital image algorithm based on optimized oriented features from accelerated segment test (FAST) and rotated binary robust independent elementary features (BRIEF) (ORB) feature matching algorithm.Firstly the adjacent frame images were preprocessed to enhance image clarity and to avoid noise interference;secondly the oriented FAST operator was used to detect feature points and the rotated BRIEF was used to describe feature points, then the neighbor hamming distance was adopted to match the ORB feature point pairs; thirdly the cascaded filter was used to remove the false matching points; finally the iterative least squares method(ILSM) was used to fit model parameters,then the motion compensation was done to achieve digital image stabilization. Standard image matching test and digital image stabilization experimental results show that the run time of compensation for each frame by the electric image stabilization method based on improved ORB algorithm is faster than 0.1 s, the positioning accuracy can reach sub-pixel level, this method can effectively compensate the translation and rotation movement between the adjacent frames, and is not sensitive to noise and illumination changes, has strong robustness. After image stabilization processing,the real scene shooting video quality obviously improves and the peak signal-to-noise ratio (PSNR) increases by an average of 10 db.