CASIA OpenIR  > 类脑智能研究中心
GAN based Sample Simulation for SEM-Image Super Resolution
Yang MK(杨茂柯)1,2; Li Guoqing(李国庆)1; Shu Chang(舒畅)1,2; Pan Zhao(赵盼)1; Hua Han(韩华)1,2
2017-10
Conference NameThe Chinese Conference on Computer Vision
Conference Date2017-10-12
Conference PlaceTianjin, China
AbstractWe propose to employ image super resolution to  accelerate collection speed of scanning electric microscopes(SEM). This process can be done by collecting images in lower resolution, and then upscale the collected images with image super-resolution algorithms. However, because of physical factors, SEM-images collected in different resolution changed not only in their scale, but also with noise level and physical distortion. Consequently, it is hard to obtain training dataset. In order to solve this problem, we designed a generative adversarial network (GAN) to fit the noise of SEM images, and then generate realistic training samples from high resolution SEM data. Finally,  a fully convolutional network have been designed to perform image super-resolution and image denoise at the same time. This pipeline works well on our SEM-image dataset.
KeywordImage Super Resolution Generative Adversarial Network Scanning Electric Microscope
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21511
Collection类脑智能研究中心
Affiliation1.中国科学院自动化研究所
2.中国科学院大学
Recommended Citation
GB/T 7714
Yang MK,Li Guoqing,Shu Chang,et al. GAN based Sample Simulation for SEM-Image Super Resolution[C],2017.
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