关键词: 运动估计, 梯度互相关, 优化滤波器, 上采样, 矩阵相乘, 核回归

Abstract: Concerning highly accurate sub-pixel motion estimation, an improved algorithm based on gradient cross-correlation with kernel regression refining is proposed. Firstly, the spatial gradient of the image is generated using an optimal filter. Secondly, an upsampled gradient cross-correlation is computed efficiently by means of matrix-multiply discrete Fourier transform, and the initial estimation of motion vector with sub-pixel accuracy procured by the peak of it. Finally, a kernel regression function is fit to the upsampled gradient cross-correlation values in a neighborhood of initial estimation, then refines the initial estimation with the location of peak found in the kernel regression fitting function, so as to obtain accurate estimation at arbitrary-precision. In the comparison experiments with some state-of-the-art algorithms, the accuracy of motion estimation of proposed scheme increases by more than 74% in the case of noise-free; while under the noise condition, the accuracy is improved by more than 68%. Experimental results show that the proposed algorithm can not only achieve good robustness to the influence of noise, but can also improve the accuracy of motion estimation significantly.

Key words: motion estimation, gradient cross-correlation, optimal filter, upsampling, matrix-multiplication, kernel regression