Tomographic Reconstruction Of Imaging Diagnostics With A Generative Adversarial Network

被引：0

作者：

Kenmochi N. ^{[2
]}

Nishiura M. ^{[1
]}

Nakamura K. ^{[2
]}

Yoshida Z. ^{[2
]}

机构：

[1] National Institute for Fusion Science, Toki

[2] The University of Tokyo, Kashiwa

来源：

Plasma Fusion Res. | 2019年 / 1-2期

基金：

日本学术振兴会;

关键词：

deep learning; generative adversarial network; imaging diagnostic; laboratory magnetosphere; tomographic reconstruction;

D O I：

10.1585/PFR.14.1202117

中图分类号：

学科分类号：

摘要：

We have developed a tomographic reconstruction method using a conditional Generative Adversarial Network to obtain local-intensity profiles from imaging-diagnostic data. To train the network we prepared pairs of local-emissivity and line-integrated images that simulate the experimental system. After validating the accuracy of the trained network, we used it to reconstruct a local image from a measured line-integrated image. We applied this procedure to the He II-emission imaging diagnostic for RT-1 magnetospheric plasmas, including the effects of stray light within the measured image to remove reflections from the chamber walls in the reconstruction. The local intensity profiles we obtain clearly elucidate the effect of ion-cyclotron-resonance heating. This method is a powerful tool for systems where it is difficult to solve the inversion problem due to the involved contributions of nonlocal optical effects or measurement restrictions. © 2019 The Japan Society of Plasma Science and Nuclear Fusion Research

引用

页码：1 / 2

页数：1

共 12 条

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