| Autor: |
Zhiteng WANG, Tianyi MAO, Xin ZHANG, Shujin ZHU, Jianjian ZHU, Xiubin DAI |
| Jazyk: |
English<br />Chinese |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
CT Lilun yu yingyong yanjiu, Vol 31, Iss 3, Pp 317-327 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1004-4140 |
| DOI: |
10.15953/j.ctta.2021.070 |
| Popis: |
Generative adversarial U-net for coded aperture computed tomography (CT) is proposed in this paper to alleviate the tradeoff between the non-continuous sparse projections and the ill-posedness iterative reconstruction problem. A non-continuous sparse projection model is presented based on generative adversarial U-net and the corresponding joint penalty function is formulated. Simulations using real datasets show that CT images with 256×256 pixels can be reconstructed with peak signal-to-noise ration more than 30 dB at only 5% transmittance. Furthermore, the computational time in the reconstructions is reduced by two orders of magnitude when compared with the state-of-the-art iterative algorithms in coded aperture computed tomography. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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