Dynamic Range Compression Self-Adaption Method for SAR Image Based on Deep Learning
Abstract
:1. Introduction
2. Proposed Method
2.1. Overview of Proposed Decomposition-Fusion Framework
2.2. Bilateral Feature Enhancement Module
2.2.1. High Frequency Feature Branch
2.2.2. Low Frequency Feature Branch
2.3. Feature Fusion Module
2.3.1. Residual Recursive Learning Unit
2.3.2. Multi-Scale Attention Mechanism
2.4. Loss Function
3. Experiments and Analysis
3.1. Implementation Details
3.2. Evaluation Metrics
3.3. Results and Analysis
3.3.1. Experiments on Synthesized SAR Images
3.3.2. Experiments on Real-world SAR Images
3.4. Ablation and Analysis
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviation
Abbreviation | Definition |
SAR | Synthetic Aperture Radar |
HDR | High Dynamic Range |
LDR | Low Dynamic Range |
CAM | Channel Attention Module |
RRL | Residual Recursive Learning Unit |
MA | Multi-scale Attention Module |
HB | High frequency feature Branch |
LB | Low frequency feature Branch |
BM | Bilateral feature enhancement Module |
FM | Feature fusion Module |
PB | Preprocessing Block |
EB | Encoder Block |
MB | Middle Block |
DB | Decoder Block |
OB | Output Block |
PSNR | Peak Signal-to-Noise Ratio |
SSIM | Structural Similarity Index |
HE | Histogram Equalization |
CLAHE | Contrast Adaptive Limitation Histogram Equalization |
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Image 1 | Image 2 | Image 3 | Image 4 | |||||
---|---|---|---|---|---|---|---|---|
PSNR | SSIM | PSNR | SSIM | PSNR | SSIM | PSNR | SSIM | |
HE | 6.572 | 0.527 | 7.397 | 0.493 | 7.324 | 0.490 | 6.721 | 0.587 |
CLAHE | 25.293 | 0.584 | 25.431 | 0.572 | 25.445 | 0.571 | 25.240 | 0.589 |
Retinex | 20.663 | 0.753 | 20.675 | 0.781 | 20.142 | 0.774 | 20.146 | 0.745 |
Gamma | 21.193 | 0.538 | 21.690 | 0.574 | 21.031 | 0.576 | 21.832 | 0.508 |
Log | 23.162 | 0.531 | 23.532 | 0.585 | 23.043 | 0.581 | 23.849 | 0.517 |
Proposed | 31.308 | 0.845 | 31.774 | 0.890 | 31.866 | 0.894 | 31.267 | 0.838 |
Image 1 | Image 2 | Image 3 | ||||
---|---|---|---|---|---|---|
Entropy | EME | Entropy | EME | Entropy | EME | |
HE | 6.195895 | 0.922171 | 5.493693 | 0.920750 | 3.406962 | 0.921220 |
CLAHE | 8.533201 | 0.791921 | 8.103221 | 0.778417 | 4.734564 | 0.741041 |
Retinex | 5.172613 | 0.858365 | 4.164617 | 0.829591 | 1.321904 | 0.798544 |
Gamma | 3.707392 | 0.7473 | 3.059469 | 0.7421 | 0.874383 | 0.7116 |
Log | 3.695861 | 0.8301 | 3.034859 | 0.8188 | 0.870504 | 0.8031 |
Proposed | 7.779042 | 0.921855 | 10.72357 | 0.920489 | 8.081626 | 0.920724 |
PSNR | SSIM | Entropy | EME | |
---|---|---|---|---|
HB + FM | 28.554 | 0.757 | 2.938 | 0.644 |
LB + FM | 27.058 | 0.799 | 4.068 | 0.843 |
BM (HB + LB) | 28.983 | 0.808 | 4.417 | 0.828 |
BM + FM without MA | 30.341 | 0.810 | 5.081 | 0.887 |
BM + FM with MA(Ours) | 31.817 | 0.848 | 5.497 | 0.899 |
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Shi, H.; Sheng, Q.; Wang, Y.; Yue, B.; Chen, L. Dynamic Range Compression Self-Adaption Method for SAR Image Based on Deep Learning. Remote Sens. 2022, 14, 2338. https://doi.org/10.3390/rs14102338
Shi H, Sheng Q, Wang Y, Yue B, Chen L. Dynamic Range Compression Self-Adaption Method for SAR Image Based on Deep Learning. Remote Sensing. 2022; 14(10):2338. https://doi.org/10.3390/rs14102338
Chicago/Turabian StyleShi, Hao, Qingqing Sheng, Yupei Wang, Bingying Yue, and Liang Chen. 2022. "Dynamic Range Compression Self-Adaption Method for SAR Image Based on Deep Learning" Remote Sensing 14, no. 10: 2338. https://doi.org/10.3390/rs14102338