# V-Shaped Dense Denoising Convolutional Neural Network for Electrical Impedance Tomography

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/v-shaped-dense-denoising-convolutional-neural-network-for-electrical-impedance/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Xinyu Zhang,Zichen Wang,Rong Fu,Di Wang,Xiaoyan Chen,Xiaoyong Guo,Huaxiang Wang |
| Citations | 46 |
| DOI | 10.1109/tim.2022.3166177 |
| Fields | Engineering |
| Open Access | false |
| OA Status | closed |
| OpenAlex ID | https://openalex.org/W4226206947 |
| Type | article |
| Year | 2022 |

## Paper authors

- [Rong Fu](https://scholariq.org/researchers/rong-fu/)

## Paper journal

- [IEEE Transactions on Instrumentation and Measurement](https://scholariq.org/journals/ieee-transactions-on-instrumentation-and-measurement/)

## Paper primary topic

- [Electrical and Bioimpedance Tomography](https://scholariq.org/topics/electrical-and-bioimpedance-tomography/)

## Paper topics

- [Electrical and Bioimpedance Tomography](https://scholariq.org/topics/electrical-and-bioimpedance-tomography/)
- [Flow Measurement and Analysis](https://scholariq.org/topics/flow-measurement-and-analysis/)
- [Non-Destructive Testing Techniques](https://scholariq.org/topics/non-destructive-testing-techniques/)

---
Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.