CASIA OpenIR  > 毕业生  > 硕士学位论文
Thesis Advisor韩华
Degree Grantor中国科学院研究生院
Place of Conferral北京
Keyword扫描电镜 深度学习 图像超分辨率重建
Other Abstract
We propose to employ image super resolution to  accelerate collection speed of scanning electric microscopes(SEM). This process can be done by collecting images in a relatively lower resolution, and then upscale the collected images with image super-resolution(SR) algorithms. For the SEM-images, current state-of-the-art methods are good to use. However, because of the purpose of employ SR algorithms to deal with SEM images is to accelerate the collection process, some difference are included in the application.
There are ome huge difference between the normal nature images and SEM images. First of all, for a same size of an image patch, SEM images have less useful information than nature images. Thus, structure and details of SEM images may need larger receptive field to represent. Besides, the noise in SEM images cannot be ignored, otherwise it will disturb the reconstruction process of SR algorithm.
The characteristic of SEM images makes it hard to obtain suitable training dataset to train a SR model mapping between noised low resolution images and unnoised high resolution images. In order to bridge the gap, two kind of methods is proposed, and named Noise-GAN and NENet respectively. And a pipeline is proposed to generate suitable training data for SR models. Finally,  two state-of-the-art SR models is employed, and one new SR model is designed to do experiments on our application. Results illustrate that our pipeline works well on SEM-image datasets.
Document Type学位论文
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
杨茂柯. 基于深度学习的电镜图像超分辨率技术研究与应用[D]. 北京. 中国科学院研究生院,2018.
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