Multi defect detection and analysis of electron microscopy images with deep learning

Multi-defect microscopy image restoration under limited data conditions

Deep learning methods are becoming widely used for restoration of defects associated with fluorescence microscopy imaging. One of the major challenges in application of such methods is the availability of training data. In this work, we propose a unified method for reconstruction of multi-defect fluorescence microscopy images when training data is limited. Our approach consists of two stages: first, we perform data augmentation using Generative Adversarial Network (GAN) with conditional instance normalization (CIN); second, we train a conditional GAN (cGAN) on paired ground-truth and defected …

Fully automated primary particle size analysis of agglomerates on transmission electron microscopy images via artificial neural networks

There is a high demand for fully automated methods for the analysis of primary particle size distributions of agglomerates on transmission electron microscopy images. Therefore, a novel method, based on the utilization of artificial neural networks, was proposed, implemented and validated. The training of the artificial neural networks requires large quantities (up to several hundreds of thousands) of transmission electron microscopy images of agglomerates consisting of primary particles with known sizes. Since the manual evaluation of such large amounts of transmission electron microscopy ima…

Segmentation of scanning electron microscopy images from natural rubber samples with gold nanoparticles using starlet wavelets

Electronic microscopy has been used for morphology evaluation of different materials structures. However, microscopy results may be affected by several factors. Image processing methods can be used to correct and improve the quality of these results. In this paper we propose an algorithm based on starlets to perform the segmentation of scanning electron microscopy images. An application is presented in order to locate gold nanoparticles in natural rubber membranes. In this application, our method showed accuracy greater than 85% for all test images. Results given by this method will be used in…

结合无监督与监督学习在显微成像中实现完整4H-SiC晶圆的缺陷分析

Combining unsupervised and supervised learning in microscopy enables defect analysis of a full 4H-SiC wafer

本文结合无监督与监督学习及数据挖掘技术，构建自动化图像分析流程，用于从约4万张图像拼接的KOH腐蚀4H-SiC晶圆显微图中提取全部缺陷的类型与位置。

Machine Learning Pipeline for Segmentation and Defect Identification from High Resolution Transmission Electron Microscopy Data

In the field of transmission electron microscopy, data interpretation often lags behind acquisition methods, as image processing methods often have to be manually tailored to individual datasets. Machine learning offers a promising approach for fast, accurate analysis of electron microscopy data. Here, we demonstrate a flexible two step pipeline for analysis of high resolution transmission electron microscopy data, which uses a U-Net for segmentation followed by a random forest for detection of stacking faults. Our trained U-Net is able to segment nanoparticle regions from amorphous background…

用于金属网缺陷检测的长工作距离便携式智能手机显微镜

Long working distance portable smartphone microscopy for metallic mesh defect detection

针对现有手机显微镜不适用于工业原位检测的问题，提出长工作距离反射式手机显微镜系统LD-RSM及双先验加权RPCA缺陷检测方法，实现了高准确率的金属网缺陷检测，具备工业原位在线检测应用潜力。