Name
Abstract | ||
|---|---|---|
Speckle noise is an inherent nature of synthetic aperture radar (SAR) images, which degrades the quality of the images and makes the interpretation of SAR images difficult. In this letter, we propose a despeckling method by simultaneously exploring low-rank prior and multi-scale prior of SAR images. Especially, we propose a low-rank minimization model by considering a data fidelity term derived from the Fisher–Tippett distribution and a weighted nuclear norm regularization term. Furthermore, we explore the multi-scale prior by selecting similar patches from different scales of the SAR image. The resulting optimization problem is solved by the alternating direction method of multipliers (ADMM). Experiments conducted on both simulated and real SAR images demonstrate that the proposed method can provide promising despeckling results in terms of speckle reduction and texture and edge details preservation. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/LGRS.2019.2926196 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Radar polarimetry,Speckle,Minimization,Synthetic aperture radar,Aggregates,Optimization,Image edge detection | Computer vision,Artificial intelligence,Mathematics | Journal |
Volume | Issue | ISSN |
17 | 3 | 1545-598X |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongdong Guan | 1 | 4 | 4.79 |
| Deliang Xiang | 2 | 12 | 5.27 |
| Xiaoan Tang | 3 | 36 | 8.24 |
| Gangyao Kuang | 4 | 37 | 8.32 |