Research On Image Stabilization Algorithm Based On Background Feature Matching

Digital image stabilization algorithm is an ideal video sequence stabilization technique.It is a hotspot in the development of video stabilization as it has a large number of advantages,such as low cost,small size,good compatibility and easy migration compared to traditional mechanical stabilization algorithm and optical stabilization algorithm.Since the camera system has many application environments,video shaky forms are different.Therefore,designing different image stabilization systems based on their characteristics of various shaky forms is the priority in image stabilization algorithm development.On the basis of background features matching,this dissertation focuses on digital image stabilization algorithms of three shaky types with details as follows:(1)For the case of camera’s translational shake,a fast digital image stabilization algorithm based on constrained one-bit transform(C-1BT)is proposed.On the basis of classical block matching and adaptive rood pattern search(ARPS)algorithm,this paper introduces two ideas of C-1BT transform and background region feature pre-selection to improve the performance of traditional matching algorithm from three aspects including sub-block location,matching criteria and search process.Firstly,stable background region pre-selection method is introduced to ensure the quality of sub-blocks set,which can discard some sub-blocks that do not contribute to solving global motion parameters.At the same time,traditional floating-point operations are replaced by logic operation of bit-plane in the matching process,which can increase hardware portability and reduce computational burden;Secondly,ARPS is used to reduce full search(FS)process computation burden,and meanwhile both the arm length prediction and search shape of ARPS are adjusted according to the characteristics of global motion consistency,which ensures a better balance between block-matching accuracy and operation speed;At last,global translational motion parameters are obtained based on statistical rules,and then adaptive motion vector integration(MVI)algorithm is used to extract compensation parameters and remove jitter components of the current video sequence.Simulation results show that both the accuracy and speed of this algorithm are improved to some extent compared to those using classical block matching algorithm.(2)For the case of camera’s small-amplitude complex shake,a digital image stabilization algorithm based on improved Noble corner matching is proposed.Global motion estimation algorithm plays an important role in affecting the performance of digital image stabilization system,so in order to balance the real-time performance and accuracy in digital image stabilization,a digital image stabilization algorithm based on improved Noble feature matching is used as the basic algorithm to achieve the stabilization of shaky video sequence in accordance with background region preselection,feature matching,parameter solution and motion compensation.Firstly,the absolute differences between corresponding image sub-blocks are calculated,and the global consistency of background pixels is used to eliminate unreliable regions.Meanwhile,adaptive detection threshold values are set in accordance with neighborhood average gradient to extract stable background features.The adaptive threshold values can not only provide a sufficiently large number of feature sets but also ensure the uniformly distribution of features in the retained image regions;Secondly,the neighborhood gradient information of the feature point is used again to build feature descriptors,rough matching sets are obtained by setting the ratio of the closest neighbor to second-closest neighbor,and initial matching is optimized by mean space distance criteria;Finally,global motion parameters are solved based on image transformation model,and compensation for shaky images is completed by Kalman filter.Simulation results show that this algorithm can stabilize video images quickly and accurately with the peak signal-to-noise ratio(PSNR)increased by over 2dB on average,and its performance is especially excellent when the rotation angle is less than 5°.(3)For the case of the camera’s large amplitude complex shake,a digital image stabilization algorithm based on speed-up robust features(SURF)is proposed.Because of strong robustness,SURF has excellent performance in video processing and computer vision,but as it has high complexity,its time performance still needs to be further improved when applied to real-time systems.To compensate for this defect,the algorithm introduces two pre-selection operations including queen pattern and local entropy value which can be selected based on actual requirements and image sizes in applications to effectively reduce the time for building feature descriptors;Secondly,in the matching process,traditional Euclidean distance calculation method is replaced by vector inner product distance criterion,which can effectively reduce computational burden under the premise of ensuring feature matching accuracy.Meanwhile cascade filters are built based on the distance ratio of the vector inner product from the closest neighbor to the second-closest neighbor and the nature of feature points to keep the background feature suitable for global motion trend and ensure the accuracy and globality of matching sets as much as possible;Finally,iterative least squares method is used to solve the camera’s affine model parameters,and compensating parameters of video are obtained on the basis of smooth results of motion parameters of Gaussian filter.Simulation results show that this algorithm can effectively and timely stabilize video sequence and save much more time for calculation compared with traditional SURF algorithm.