This tutorial review paper consolidates the existing applications of the power watershed (PW) optimization framework in the context of image processing. In the literature, it is known that PW framework when applied to some well-known graph-based image segmentation and filtering algorithms such as random walker, isoperimetric partitioning, ratio-cut clustering, multi-cut and shortest path filters yield faster yet consistent solutions. In this paper, the intuition behind the working of PW framework, i.e. exploitation of contrast invariance on image data is explained. The intuitions are illustrated with toy images and experiments on simulated astronomical images. This article is primarily aimed at researchers working on image segmentation and filtering problems in application areas such as astronomy where images typically have huge number of pixels. Classic graph-based cost minimization methods provide good results on images with small number of pixels but do not scale well for images with large number of pixels. The ideas from the article can be adapted to a large class of graph-based cost minimization methods to obtain scalable segmentation and filtering algorithms.

本教程评论论文巩固了电力分水岭 (PW) 优化框架在图像处理环境中的现有应用。在文献中，众所周知，当 PW 框架应用于一些著名的基于图的图像分割和过滤算法时，例如随机游走、等周分区、比率切割聚类、多切割和最短路径滤波器，可以产生更快但一致的解决方案. 在本文中，解释了 PW 框架工作背后的直觉，即利用图像数据的对比度不变性。用玩具图像和模拟天文图像的实验来说明直觉。本文主要针对研究应用领域中的图像分割和过滤问题的研究人员，例如天文学，其中图像通常具有大量像素。经典的基于图的成本最小化方法在像素数较少的图像上提供了良好的结果，但对于像素数较多的图像则不能很好地缩放。文章中的想法可以适用于一大类基于图的成本最小化方法，以获得可扩展的分割和过滤算法。