A Convolutional Neural Network SAR Image Denoising Algorithm Based on Self-Learning Strategies
Due to its high resolution and all-weather imaging capability, Synthetic Aperture Radar (SAR) is widely used in fields such as Earth observation and environmental monitoring. However, SAR images are prone to noise interference during the imaging process, which seriously affects the visualization eff...
Saved in:
| Main Authors: | Jun Wang, Ke Xu |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/9/4786 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Tomographic SAR imaging via generative adversarial neural network with cascaded U‐Net architecture
by: Jie Li, et al.
Published: (2024-09-01) -
FRANet: A Feature Refinement Attention Network for SAR Image Denoising
by: Shuaiqi Liu, et al.
Published: (2025-01-01) -
Self-Activated Implicit Neural Representation for Synthetic Aperture Radar Images
by: Dongshen Han, et al.
Published: (2024-11-01) -
NBDNet: A Self-Supervised CNN-Based Method for InSAR Phase and Coherence Estimation
by: Hongxiang Li, et al.
Published: (2025-03-01) -
A Multiscale Convolution SAR Image Target Recognition Method Based on Complex-Valued Neural Networks
by: Guangyu Hou, et al.
Published: (2025-01-01)