论文标题
通过加速双重优化估计变异图像运动估计
Variational Image Motion Estimation by Accelerated Dual Optimization
论文作者
论文摘要
估计光流是计算机视觉中最有趣的问题之一，它估计了两个连续图像之间有关像素的位移的基本信息。这项工作引入了一个有效的双重优化框架，并加速了预处理，以挑战总正规化光流估计的非平滑优化问题。从理论上讲，提出的双重优化框架为给定的困难优化问题带来了优雅的变分分析，同时呈现有效的算法方案，而无需直接处理数字中相应的非平滑度。通过通过多尺度实施引入有效的预处理，与最先进的方法相比，提出的加速双重优化方法实现了图像运动的竞争估计结果。此外，我们表明所提出的预科人员可以以高效率来保证实施的数值方案的收敛。
Estimating optical flows is one of the most interesting problems in computer vision, which estimates the essential information about pixel-wise displacements between two consecutive images. This work introduces an efficient dual optimization framework with accelerated preconditioners to the challenging nonsmooth optimization problem of total-variation regularized optical-flow estimation. In theory, the proposed dual optimization framework brings an elegant variational analysis on the given difficult optimization problem, while presenting an efficient algorithmic scheme without directly tackling the corresponding nonsmoothness in numeric. By introducing efficient preconditioners with a multi-scale implementation, the proposed accelerated dual optimization approaches achieve competitive estimation results of image motion, comparing to the state-of-the-art methods. Moreover, we show that the proposed preconditioners can guarantee convergence of the implemented numerical schemes with high efficiency.
