SAR Image Despeckling via Neighborhood-adaptive Probabilistic Patch Based Non-local Approach

Biao Hou; GuiLin Ju; HongXiao Feng; Zhichao Liu

Chinese︱Feedback︱Save this page

• Elaborating Academic Views 　　　　 • Exchanging Innovative Ideas
• Protecting Intellectual Properties 　　• Fast Sharing Science Papers

Sponsored by the Center for Science and Technology Development of the Ministry of Education
Supervised by Ministry of Education of the People's Republic of China

Home > Papers

SAR Image Despeckling via Neighborhood-adaptive Probabilistic Patch Based Non-local Approach

Biao Hou *,GuiLin Ju,HongXiao Feng #,Zhichao Liu

Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, Xi'an, 710071, China

*Correspondence author

#Submitted by

Subject:

Funding: National Natural Science Foundation of China（No.61671350, 61173090, 61072106 and 61271302）, the National Basic Research Program (973 Program) of China（No.2013CB329402）, the National Research Foundation for the Doctoral Program of Higher Education of China（No.20130203110009）

Opened online: 9 May 2017

Accepted by: none

Citation: Biao Hou,GuiLin Ju,HongXiao Feng.SAR Image Despeckling via Neighborhood-adaptive Probabilistic Patch Based Non-local Approach[OL]. [ 9 May 2017] http://en.paper.edu.cn/en_releasepaper/content/4731588

A new neighborhood-adaptive non-local (NL) despeckling filter is proposed in this paper. An adaptive and point-wise fashion neighborhood that limits the bound of weighted pixels is designed, which is determined by an adaptive directional scales set and a new automatic similarity threshold. The set of adaptive directional scales constructs a rectangular neighborhood and the optimal scale is obtained with the proposed similarity threshold. The presented similarity is based on the probabilistic patch based similarity (PPB-similarity) measurement and deduced with a statistical Monte Carlo method. Experiment results show that our method can not only provide superior speckle removal when compared to probabilistic patch based non-local (PPB-NL) filter with fixed neighborhood, especially for its non-iterative version, but also show good performance in preserving details and texture information.

Keywords:Adaptive neighborhood; non-local approach; SAR image despeckling; probabilistic patch based (PPB) weight

For this paper

● PDF (0B)
● Revision 0 　　
● Print this paper
● Recommend this paper to a friend
● Add to my favorite list

Saved Papers

Please enter a name for this paper to be shown in your personalized Saved Papers list

Tags

Add yours

Related Papers

Statistics

PDF Downloaded	25
Bookmarked	0
Recommend	0
Comments	Array

Submit your papers

Alert Name:
Alerting to:
Authentication email will be sent to your email address in 24 hours
Frequency:
Email Message Format:	Plain text Graphical(HTML)

Complete the form below and we will recommend the selected titles to your friends on your behalf. * Indicates a required field.
Your name*:
Your email address*:
Recipient's name*:
Recipient's email address*:
(multiple recipient's names and email addresses should be separated with semicolons)
Your comments:	I thought you would find the page(s) useful.

Your name:
Your email address:
Recipient's name:
Recipient's email address:
(multiple recipient's names and email addresses should be separated with semicolons)
Your comments:	I thought you would find this page useful.

Disclaimer: This message was sent to your friend using the "Send it to a friend" facility on the Sciencepaper Online’ WWW site, http://www.paper.edu.cn/en. The Sciencepaper Online is not responsible for the content of this email, and anything said in this email does not necessarily reflect the Sciencepaper Online's views.

	Check out RSS, or use RSS reader to subscribe this item

Saved Papers