Atmospheric Turbulence Degraded-Image Restoration by Kurtosis Minimization

被引：32

作者：

Li, Dalong ^{[1
]}

Simske, Steven ^{[2
]}

机构：

[1] Pegasus Imaging Corp, Tampa, FL 33603 USA

[2] Hewlett Packard Labs, Print Prod Automat Lab, Ft Collins, CO 80528 USA

来源：

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS | 2009年 / 6卷 / 02期

关键词：

Atmospheric turbulence; blur identification; image restoration; kurtosis; BLUR IDENTIFICATION;

D O I：

10.1109/LGRS.2008.2011569

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

Atmospheric turbulence is caused by the random fluctuations of the refraction index of the medium. It can lead to blurring in images acquired from a long distance away. Since the degradation is often not completely known, the problem is viewed as blind image deconvolution or blur identification. Our previous work has observed that blurring increases kurtosis and introduced a new blur identification method based on kurtosis minimization (KM). In this letter, this observation has been studied using phase correlation. The KM method is compared with two other signal processing methods. The limitation of the method is also discussed.

引用

页码：244 / 247

页数：4

共 10 条

[1] Noniterative blind data restoration by use of an extracted filter function [J].