论文标题
通过应用到光流的应用程序优化的在线优化
Predictive online optimisation with applications to optical flow
论文作者
论文摘要
在线优化围绕正在解决问题的新数据围绕着解决问题；考虑到更多的培训样本可用。我们将这个想法调整为动态逆问题，例如使用光流的视频处理。我们引入了相应的预测在线原始偶近距离分裂方法。视频帧现在完全对应于算法迭代。用户处方的预测变量描述了原始变量的演变。为了证明收敛性，我们需要基于（近端）梯度流的双重变量的预测变量。这会影响该方法渐近最小化的模型。我们表明，对于反问题，基本上是基于与时间耦合的静态正规机构的虚拟卷积构建新的动态常规仪。我们通过证明我们方法在计算图像稳定和正则化理论方面的融合中的出色实时性能结束。
Online optimisation revolves around new data being introduced into a problem while it is still being solved; think of deep learning as more training samples become available. We adapt the idea to dynamic inverse problems such as video processing with optical flow. We introduce a corresponding predictive online primal-dual proximal splitting method. The video frames now exactly correspond to the algorithm iterations. A user-prescribed predictor describes the evolution of the primal variable. To prove convergence we need a predictor for the dual variable based on (proximal) gradient flow. This affects the model that the method asymptotically minimises. We show that for inverse problems the effect is, essentially, to construct a new dynamic regulariser based on infimal convolution of the static regularisers with the temporal coupling. We finish by demonstrating excellent real-time performance of our method in computational image stabilisation and convergence in terms of regularisation theory.
