论文标题
非凸变量问题的逆向空间迭代：连续和离散的情况
Inverse Scale Space Iterations for Non-Convex Variational Problems: The Continuous and Discrete Case
论文作者
论文摘要
非线性滤波方法允许获得图像的分解相对于非古典尺度概念，这是由于选择凸的选择，绝对是单均匀的正规剂。可以使用具有二次数据术语的经典Bregman迭代获得相关的尺度空间流。我们将Bregman迭代应用于提升，即更高维和凸功能，以将这些方法的范围扩展到具有任意数据项的功能。在连续和离散的环境中，我们为正规器的亚级别提供条件，在这些环境下，此迭代启动降低了标准的Bregman迭代。我们显示了凸和非凸状情况的实验结果。
Non-linear filtering approaches allow to obtain decompositions of images with respect to a non-classical notion of scale, induced by the choice of a convex, absolutely one-homogeneous regularizer. The associated inverse scale space flow can be obtained using the classical Bregman iteration with quadratic data term. We apply the Bregman iteration to lifted, i.e. higher-dimensional and convex, functionals in order to extend the scope of these approaches to functionals with arbitrary data term. We provide conditions for the subgradients of the regularizer -- in the continuous and discrete setting -- under which this lifted iteration reduces to the standard Bregman iteration. We show experimental results for the convex and non-convex case.
