The microscopic image is important data for recording the microstructure information of materials. Researchers usually use image processing algorithms to extract material features from that and then characterize the material microstructure. However, the microscopic images obtained by a microscope often have random damaged regions, which will cause the loss of information and thus inevitably influence the accuracy of microstructural characterization, even lead to a wrong result. To handle this problem, we provide a deep learning based fully automatic method for detecting and inpainting damaged regions in material microscopic images, which can automatically inpaint damaged regions with different positions and shapes, as well as we also use a data augmentation method to improve the performance of inpainting model. We evaluate our method on Al-La alloy microscopic images, which indicates that our method can achieve promising performance on inpainted and material microstructure characterization results compared to other image inpainting software for both accuracy and time consumption. This article is protected by copyright. All rights reserved.

中文翻译：

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基于深度学习的材料显微图像自动修复

显微图像是记录材料微观结构信息的重要数据。研究人员通常使用图像处理算法从中提取材料特征，然后表征材料微观结构。然而，显微镜获得的显微图像往往具有随机的损坏区域，这会造成信息的丢失，从而不可避免地影响显微结构表征的准确性，甚至导致错误的结果。为了解决这个问题，我们提供了一种基于深度学习的全自动材料显微图像损伤区域检测和修复方法，它可以自动修复不同位置和形状的损伤区域，并且我们还使用数据增强方法来改善材料显微图像中的损伤区域。修复模型的性能。我们在 Al-La 合金显微图像上评估了我们的方法，这表明与其他图像修复软件相比，我们的方法可以在修复和材料微观结构表征结果上实现有希望的性能，无论是精度还是时间消耗。本文受版权保护。版权所有。