基于改进粒子滤波算法的视频超分辨率重建

Video Super Resolution Reconstruction Based on Improved Particle Filtering Algorithm

视频超分辨率重建的一个必要步骤是视频运动估计,相对其他图像匹配算法,基于特征点的视频匹配算法具有更高的鲁棒性,但精确度受特征点的定位、选取和匹配误差的影响较大。为此,提出将粒子滤波应用到视频超分辨率的运动估计问题中,用粒子滤波算法来修正匹配误差,并针对粒子滤波中的粒子匮乏问题改进基本粒子滤波算法。实验结果表明,该算法比其他经典滤波算法估计精度有了较大提高,且在超分辨率重建中能更精确地进行运动估计,匹配精度和稳定性能都有所改善。

In the super resolution reconstruction,a key step is the video motion estimation. Compared with other methods,matching algorithm based on features of video has higher robustness. How ever,the accuracy of this kind of methods is affected by the position and selection of feature points. To overcome this problem,this paper introduces the particle filtering into the motion estimation to reduce the matching error. The main disadvantage of the particle filtering is particle degeneracy. In this paper,an extended Kalman filtering is used to general the proposal distribution,and an Unscented Kalman Filtering（ UKF） is used to refine particles. Experimental results show that,compared w ith other eight classic filtering algorithms,the proposed algorithm has much better performance,and for the super resolution reconstruction issue,the proposed algorithm can estimate the motion more accurately.