SAR Image Despeckling Based on GO Distribution

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 机器人理论与应用

	SAR Image Despeckling Based on GO Distribution
	Xiangli Nie; Bo Zhang; Hong Qiao; Suiwu Zheng
	2013
会议名称	International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC)
会议录名称	International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC)
会议日期	Dec 20-22, 2013
会议地点	Shenyang, China
摘要	Abstract-In this paper, we propose a new model for synthetic aperture radar (SAR) image despeckling based on the GO statistical distribution and nonlocal total variation regularization. By taking the distribution of the backscatter into account, a new data fidelity term is derived by the maximum a posteriori Bayesian rule. Combining the new fidelity term with the nonlocal total variation regularization gives a new variational model for SAR image despeckling. The primal-dual algorithm framework is then used to solve the new variational problem. Experimental results on real SAR images demonstrate the validity of the proposed method.
关键词	Synthetic Aperture Radar (Sar) G-zero Distribution Maximum a Posteriori (Map) Speckle Nonlocal Total Variation (Nl-tv) Primal-dual Algorithm
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/13004
专题	多模态人工智能系统全国重点实验室_机器人理论与应用
通讯作者	Xiangli Nie
推荐引用方式 GB/T 7714	Xiangli Nie,Bo Zhang,Hong Qiao,et al. SAR Image Despeckling Based on GO Distribution[C],2013.