GAN based Sample Simulation for SEM-Image Super Resolution
文献类型：会议论文
| 作者 | 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
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| 出版日期 | 2017-10 |
| 会议日期 | 2017-10-12 |
| 会议地点 | Tianjin, China |
| 关键词 | Image Super Resolution Generative Adversarial Network Scanning Electric Microscope |
| 英文摘要 | 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 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. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/21511]  |
| 专题 | 类脑智能研究中心_微观重建与智能分析 |
| 作者单位 | 1.中国科学院自动化研究所 2.中国科学院大学 |
| 推荐引用方式 GB/T 7714 | Yang MK,Li Guoqing,Shu Chang,et al. GAN based Sample Simulation for SEM-Image Super Resolution[C]. 见:. Tianjin, China. 2017-10-12. |
入库方式： OAI收割
来源：自动化研究所
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