改进的TV-L^1平滑光流估计

Improved TV-L^1 Algorithm for Smooth Optical Flow

提出将高斯平滑后的数据项和非局部中值滤波相结合的光流算法,以实现降噪并提高光流估计的稳健性和精度。该方法的数据项使用稳健的L1范数,通过高斯滤波对数据项平滑处理,抑制噪声干扰,并借助原始-对偶算法改善变分光流的求解效率;为进一步提高光流场的估计精度,引入了非局部中值滤波的全局优化策略;为提高算法对较大位移量估计的适应性,运用了由粗到精的金字塔方法。采用Middlebury光流数据库图像和真实场景图像对改进的TV-L1光流估计算法进行了实验验证。结果表明,提出的改进变分光流算法具有较强的稳健性,其光流估计精度优于传统的TV-L1模型算法。

An optical flow method combining Gaussian convoluted data term with non-local median filter is proposed to remove noise and consequently improve the robustness and accuracy of the optical flow estimation. Robust L1 norm is employed for construction of data term, which is smoothed with Gaussian filter to suppress noise, and primal-dual method is introduced to improve the estimation efficiency of variational optical flow. A global optimization strategy based on non-local median filter is used to further enhance the estimation accuracy. The coarse-to-fine pyramid technique is employed to improve the adaptability of the algorithm for large displacements estimation. The proposed method is evaluated by using both the Middlebury optical flow database images and real scene images. The experimental results show that the proposed method performs good robustness and accuracy in contrast with traditional TV-L1 model algorithms.