Deep learning based automatic inpainting for material microscopic images.

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Abstract

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.
This article is protected by copyright. All rights reserved.