Noise Reduction of SEM Images using U-net with SSIM Loss Function-Reference-Cited by-同舟云学术

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Noise Reduction of SEM Images using U-net with SSIM Loss Function

Published:2021-03-16 Issue:0 Volume:2021 Page:65-72
ISSN:2188-4730
Container-title:Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications
language:en
Short-container-title:Stochastic Systems Theory and its Applications (SSS)

Author:

Nagano Koshiro¹,Mukouyama Yoshiharu²,Nishimura Takashi³,Fujioka Hiroyuki⁴,Watanabe Kenji⁵,Kurita Takio⁶,Hidaka Akinori²

Affiliation:

1. Graduate School of Science and Engineering, Tokyo Denki University

2. School of Science and Engineering, Tokyo Denki University

3. Osaka Research Institute of Industrial Science and Technology

4. Department of System Management, Fukuoka Institute of Technology

5. Department of Information Technology and Human Factors, National Institute of Advanced Industrial Science and Technology

6. The Graduate School of Advanced Science and Engineering, Hiroshima University

Publisher

The Institute of Systems, Control and Information Engineers

Subject

General Medicine

Link

https://www.jstage.jst.go.jp/article/sss/2021/0/2021_65/_pdf

Reference12 articles.

1. [1] O. Ronneberger, et al., “U-Net: Convolutional Networks for Biomedical Image Segmentation”, MICCAI ,Springer, LNCS, Vol.9351, pp.234-241, 2015.

2. [2] P. Isola et al., “Image-to-Image Translation with Conditional Adversarial Networks”, CVPR2017, pp.1125-1134, 2017.

3. [3] M. Hradiš et al., “Convolutional neural networks for direct text deblurring”, BMVC2015, pp.1–13, 2015.

4. [4] H. Fujioka et al., “Understanding Deformation Motion of Colloidal Nanosheets from CLSM Images Using Deep Learning-Based Approach”, ICARCV2018, pp.192-197, 2018.

5. [5] H. Fujioka et al., “Detecting Nanosheet Objects from Noisy CLSM Images Using Deep Learning Approach”, Key Engineering Materials , Vol.804, pp.11-15, 2019.

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