Fast and High-Quality 3-D Terahertz Super-Resolution Imaging Using Lightweight SR-CNN
Abstract
:1. Introduction
- (1)
- A fast and high-quality 3-D SR imaging method is proposed. Compared with the method based on sparsity regularization, the imaging time is reduced by two orders of magnitude and imaging quality is improved obviously.
- (2)
- A lightweight CNN is designed, which reduces the model parameters and computation significantly. The training model can achieve satisfactory convergence under small datasets and the accuracy can reasonably improve.
- (3)
- The input and output of SR-CNN both are complex data. The phenomenon that the performance of dividing complex data into real part and imaginary part is better than that of amplitude and phase is found.
2. Methodology
2.1. Input and Output Data Generation of SR-CNN
2.2. Network Structure of SR-CNN
2.3. Simution and Training Details
3. Results
3.1. EXP1: Resolution Analysis of Different Methods
3.2. EXP2: Electromagnetic Computation Simulation of Aircraft A380
3.3. Performance Analysis for Anti-Noise Ability and Imaging Time
3.4. Ablation Experiments of Lightweight Network Structure
3.5. Comparison with Methods Based on Neural Network
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Method | Time Needs (s) | |
---|---|---|
EXP 1 | EXP 2 | |
IFFT wo win | 0.064 | 0.084 |
IFFT w win | 0.064 | 0.084 |
BPDN | 130.144 | 227.142 |
SR-CNN | 0.906 | 0.965 |
Network | Connection | Dataset Size | ||
---|---|---|---|---|
Direct Connection | Fire Module | 500 | 2000 | |
Direct-500 | √ | √ | ||
Fire-500 | √ | √ | ||
Direct-2000 | √ | √ | ||
Fire-2000 | √ | √ |
Method | RMSE × 1000 (SNR = −10 dB) | RMSE × 1000 (SNR = 0 dB) | RMSE × 1000 (SNR = 10 dB) |
---|---|---|---|
The method in [37] | 5.46 | 4.81 | 4.48 |
SR-CNN | 4.63 | 4.46 | 4.32 |
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Fan, L.; Zeng, Y.; Yang, Q.; Wang, H.; Deng, B. Fast and High-Quality 3-D Terahertz Super-Resolution Imaging Using Lightweight SR-CNN. Remote Sens. 2021, 13, 3800. https://doi.org/10.3390/rs13193800
Fan L, Zeng Y, Yang Q, Wang H, Deng B. Fast and High-Quality 3-D Terahertz Super-Resolution Imaging Using Lightweight SR-CNN. Remote Sensing. 2021; 13(19):3800. https://doi.org/10.3390/rs13193800
Chicago/Turabian StyleFan, Lei, Yang Zeng, Qi Yang, Hongqiang Wang, and Bin Deng. 2021. "Fast and High-Quality 3-D Terahertz Super-Resolution Imaging Using Lightweight SR-CNN" Remote Sensing 13, no. 19: 3800. https://doi.org/10.3390/rs13193800
APA StyleFan, L., Zeng, Y., Yang, Q., Wang, H., & Deng, B. (2021). Fast and High-Quality 3-D Terahertz Super-Resolution Imaging Using Lightweight SR-CNN. Remote Sensing, 13(19), 3800. https://doi.org/10.3390/rs13193800