We propose the use of fractals as a means of efficient data augmentation. Specifically, we employ plasma fractals for adapting global image augmentation transformations into continuous local transforms. We formulate the diamond square algorithm as a cascade of simple convolution operations allowing efficient computation of plasma fractals on the GPU. We present the TorMentor image augmentation framework that is totally modular and deterministic across images and point-clouds. All image augmentation operations can be combined through pipelining and random branching to form flow networks of arbitrary width and depth. We demonstrate the efficiency of the proposed approach with experiments on document image segmentation (binarization) with the DIBCO datasets. The proposed approach demonstrates superior performance to traditional image augmentation techniques. Finally, we use extended synthetic binary text images in a self-supervision regiment and outperform the same model when trained with limited data and simple extensions.

本研究提出了一种基于分形的高效数据增强方法，通过等离子体分形将全局图像增强转化为连续的本地变换，并采用Diamond-Square算法作为简单卷积操作级联的分形计算方式。由此构建了全模块化的、可组合的图片增强框架TorMentor，证明了该方法在文档图像分割等任务中，以及在使用扩展合成二元文本图像进行自监控时优于传统的图像增强技术。

TorMentor：基于确定性动态路径和分形的数据增强