Non-linear filtering approaches allow to obtain decompositions of images with respect to a non-classical notion of scale. The associated inverse scale space flow can be obtained using the classical Bregman iteration applied to a convex, absolutely one-homogeneous regularizer. In order to extend these approaches to general energies with non-convex data term, we apply the Bregman iteration to a lifted version of the functional with sublabel-accurate discretization. We provide a condition for the subgradients of the regularizer under which this lifted iteration reduces to the standard Bregman iteration. We show experimental results for the convex and non-convex case.

翻译：非线性过滤法能够获得与非古典规模概念有关的图像分解。 相关的反比例空间流动可以通过适用于直方、 绝对一对一的正统变异器的古典Bregman迭代法获得。 为了将这些方法扩展至非直方数据术语的一般能量, 我们将Bregman迭代法应用于与亚标签分解的解除版本功能。 我们为调试器的次梯度提供了条件, 使这种升动降低到标准的 Bregman 迭代法。 我们为 convex 和非civex 案例展示了实验结果 。