论文标题
用深卷积生成对抗网络介入微观结构的两种方法
Two approaches to inpainting microstructure with deep convolutional generative adversarial networks
论文作者
论文摘要
成像对于材料的表征至关重要。但是，即使采用样品制备和显微镜校准，成像技术也通常容易出现缺陷和不必要的人工制品。对于要用于模拟或特征分析的应用程序，这尤其有问题，因为缺陷可能导致结果不准确。微观结构介绍是一种通过用匹配边界代替合成微观结构的遮挡区域来减轻此问题的方法。在本文中，我们介绍了两种使用生成的对抗网络来通过从未分布的数据中学习微结构分布来生成任意形状和大小的偏见区域的方法。我们发现一种从高速和简单性中受益，而另一个则在介入边界处赋予更平滑的边界。我们还概述了图形用户界面的开发，该界面允许用户在“无代码”环境中使用这些机器学习方法。
Imaging is critical to the characterisation of materials. However, even with careful sample preparation and microscope calibration, imaging techniques are often prone to defects and unwanted artefacts. This is particularly problematic for applications where the micrograph is to be used for simulation or feature analysis, as defects are likely to lead to inaccurate results. Microstructural inpainting is a method to alleviate this problem by replacing occluded regions with synthetic microstructure with matching boundaries. In this paper we introduce two methods that use generative adversarial networks to generate contiguous inpainted regions of arbitrary shape and size by learning the microstructural distribution from the unoccluded data. We find that one benefits from high speed and simplicity, whilst the other gives smoother boundaries at the inpainting border. We also outline the development of a graphical user interface that allows users to utilise these machine learning methods in a 'no-code' environment.
