期刊
SIGNAL PROCESSING-IMAGE COMMUNICATION
卷 90, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.image.2020.116061
关键词
Super resolution; Synthetic aperture radar; Speckle noise; Sparse representation; Total variation; Feature space
The paper introduces a novel variational convex optimization model for single SAR image super resolution reconstruction with speckle noise, utilizing MAP estimator and effective regularization based on a combination of sparse representation, total variation (TV), and a feature space based soft projection tool. Experimental results demonstrate the efficacy of the proposed method in terms of fidelity and visual perception.
Super resolution (SR) is an attractive issue in image processing. In the synthetic aperture radar (SAR) image, speckle noise is a crucial problem that is multiplicative. Therefore, numerous custom SR methods considering additive Gaussian noise cannot respond to this image degradation model. The main contribution of this paper is to propose a novel variational convex optimization model for the single SAR image SR reconstruction with speckle noise that is one of the first works in this field. Employing maximum a posteriori (MAP) estimator and proposing an effective regularization based on combination of sparse representation, total variation (TV) and a novel feature space based soft projection tool to use merits of them is the main idea. To solve the proposed model, the split Bregman algorithm is employed efficiently. Experimental results for the multiple synthetic and realistic SAR images show the effectiveness of proposed method in terms of both fidelity and visual perception.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据