中文摘要：

根据估计量的统计特性，提出了一种适用于低信噪比条件下运动估计的最小化MSE（均方误差）滤波器多尺度运动估计算法．首先，根据Cramer-Rao下界建立一个包含估计量噪声项的MSE惩罚函数．然后，最小化MSE惩罚函数设计一种用于低信噪比条件下运动估计的优化滤波器．该优化滤波器与多尺度方法相结合，使其对低信噪比条件下运动估计的精度得到了进一步提高．实验模拟表明，该方法在估计2个像素附近的噪声图像运动时，估计偏差小于0．008个像素．与传统方法相比，本文方法对低信噪比条件下的运动估计具有更高的估计精度．

英文摘要：

A motion estimation algorithm based on gradient methods for low signal-to-noise （SNR） scenarios is presented by using statistical performance of the estimator.Firstly, the cost function of mean square error （MSE） is developed based on Cramer- Rao low bound by considering the influence of the noises on motion estimation. Secondly, the motion estimation MSE is minimized to find the gradient optimal filters.In combination with multi-scale pyramid approach,the estimator accuracy of such an algorithm can be further improved. Experimental simulations show that the estimator bias is less than 0.008 pixels for large motion estimation of low SNR scenarios. This represents a significant decrease in estimator accuracy compared to existing methods for motion estimation of low SNR situations.