.png){kind=icon}
.png){kind=icon}
Return
Collaborative Optimization for SAR Image Despeckling With Structure Preservation
.png){kind=icon}
PRE
AI
DOI：10.1109/TGRS.2024.3524208.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Speckle is a significant problem in synthetic aperture radar (SAR) image processing, as it deteriorates the texture details and thus limits the precision of its high-accuracy applications. The impact of speckle on SAR images is related to their structural characteristics, which usually classify the images into homogeneous regions, heterogeneous regions, and extremely heterogeneous regions. In the latter two regions, structures such as edges and strong point targets generate a more complex speckle distribution which differs from that in homogeneous regions. Despeckling filters based on multiplicative models perform well in homogeneous regions but exhibit poor performance in the other two types of regions. To solve this problem, a collaborative optimization for SAR image despeckling with structure preservation is proposed in this article, which leverages the coupling between the despeckling module and the structure extraction module. Guided by structure information, the proposed method achieves better preservation of edges and texture details while removing speckle. Experimental results based on real SAR images demonstrate that the proposed method effectively enhances the despeckling performance in all three types of regions.
Keywords:
Speckle
Radar polarimetry
Optimization
Image edge detection
Collaboration
Feature extraction
Data mining
Noise
Synthetic aperture radar
Optical sensors
Collaborative optimization
deep learning
despeckling
structure
synthetic aperture radar (SAR)
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W