HSFMamba: Hierarchical Selective Fusion Mamba Network for Optics-Guided Joint Super-Resolution and Denoising of Noise-Corrupted SAR Images
Synthetic Aperture Radar (SAR) image interpretation has attracted widespread attention in remote sensing applications. However, the performance of existing methods is severely hindered by inherent limitations of SAR imaging mechanisms, such as speckle noise and low resolution. With the continuous ad...
Saved in:
| Main Authors: | Zhicheng Zhao, Qing Gao, Jinquan Yan, Chenglong Li, Jin Tang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11045185/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Convolutional Neural Network SAR Image Denoising Algorithm Based on Self-Learning Strategies
by: Jun Wang, et al.
Published: (2025-04-01) -
SAR Image Super-Resolution Based on Multiscale Edge Texture-Oriented GAN Approach
by: Yunfei Zhu, et al.
Published: (2025-01-01) -
FRANet: A Feature Refinement Attention Network for SAR Image Denoising
by: Shuaiqi Liu, et al.
Published: (2025-01-01) -
DADSR: Degradation-Aware Diffusion Super-Resolution Model for Object-Level SAR Image
by: Zilong Chen, et al.
Published: (2025-01-01) -
Performance Boundaries and Tradeoffs in Super-Resolution Imaging Technologies for Space Targets
by: Xiaole He, et al.
Published: (2025-02-01)