【摘要】 针对噪声影响下光流计算稳健性较差及收敛速度慢的问题,提出一种噪声环境下光流场快速稳健估计方法。所提算法基于噪声环境下光流场估计方法,引入惩罚因子以增强光流计算稳健性,并在光流计算迭代公式中加入动量因子缩短光流计算收敛时间以加快光流场计算。而后基于变分方法极小化光流能量函数求解欧拉-拉格朗日方程,最后通过迭代方法求得速度场。仿真结果表明,对视频中连续两帧图片加入不同高斯噪声后,与M算法及ML算法相比,所提算法可显著增强光流场计算稳健性,缩短光流计算收敛时间,加快光流场计算。更多还原

【Abstract】 To address the issue of poor robustness and slow convergence speed in the calculation of optical flow under the influence of noise, a fast robust method for the optical flow field estimation in noisy environment is proposed. Based on the estimation method of optical flow in noisy environment, a penalty factor is introduced to enhance the robustness of the calculation of optical flow, a momentum factor is added to the iterative formula of optical flow calculation to shorten the convergence time of optical flow calculation, and then the calculation of the optical flow field is accelerated. The Euler-Lagrange equation is solved by minimizing the energy function of optical flow on the basis of the variation principle. Finally, the velocity field is obtained by using the iterative method. Simulation results show that, as compared to the M algorithm and the ML algorithm, the proposed algorithm can enhance the robustness of the optical flow considerably, shorten the convergence time of optical flow calculation and speed up the calculation of the optical flow field, after adding two different Gaussian noises to two consecutive frames in the video.更多还原