Name
Abstract | ||
|---|---|---|
In this paper, we propose a new synthetic aperture radar (SAR) image despeckling method based on the nonlocal low-rank minimization model. First, some similar image patches are selected for each pixel to construct the patch group matrix (PGM). Then, a new low-rank minimization model, called Fisher–Tippett distribution (FT)-weighted nuclear norm minimization (WNNM), is proposed to recover the underlying low-rank component from the PGM. Specifically, the FT-WNNM is developed by reformulating the despeckling problem as the maximizing
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probability problem. The new model consists of a data fidelity term and a regularization term (also called prior term). The data fidelity term is derived from the statistical distribution of SAR images in the logarithm domain, which is known as the Fisher–Tippett distribution, and the regularization term is the recent weighted nuclear norm. Then, the alternating direction method of multipliers (ADMM) is introduced to solve the corresponding optimization problem. Under ADMM framework, the resulting subproblems can be solved efficiently and the convergence can be guaranteed. Extensive experiments on both simulated and real SAR images demonstrate that the proposed method can achieve comparable or even better despeckling performance than some state-of-the-art despeckling algorithms. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TGRS.2018.2885089 | IEEE Transactions on Geoscience and Remote Sensing |
Keywords | Field | DocType |
Synthetic aperture radar,Minimization,Image denoising,Speckle,Convex functions,Noise measurement,Optimization | Convergence (routing),Computer vision,Synthetic aperture radar,Algorithm,Matrix norm,Posterior probability,Regularization (mathematics),Artificial intelligence,Pixel,Logarithm,Optimization problem,Mathematics | Journal |
Volume | Issue | ISSN |
57 | 6 | 0196-2892 |
Citations | PageRank | References |
0 | 0.34 | 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 |