作者投稿和查稿 主编审稿 专家审稿 编委审稿 远程编辑

计算机工程 ›› 2011, Vol. 37 ›› Issue (2): 215-217. doi: 10.3969/j.issn.1000-3428.2011.02.075

• 图形图像处理 • 上一篇    下一篇

基于小波域HMT的图像杂波抑制方法

赖宗英,艾斯卡尔?艾木都拉   

  1. (新疆大学信息科学与工程学院,乌鲁木齐 830046)
  • 出版日期:2011-01-20 发布日期:2011-01-25
  • 作者简介:赖宗英(1986-),女,硕士,主研方向:信号与信息处理;艾斯卡尔?艾木都拉,教授、博士后、博士生导师

Image Clutter Suppression Method Based on Wavelet Domain HMT

LAI Zong-ying, Askar Hamdulla   

  1. (College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China)
  • Online:2011-01-20 Published:2011-01-25

摘要:

针对复杂背景下红外微弱点状运动目标的检测,提出一种基于小波域HMT模型的图像杂波抑制方法。对图像小波系数低频部分建立隐马尔可夫树模型,使用Bayesian准则估计图像背景小波系数,参照杂波抑制模型,得到杂波抑制后图像的信号加噪声模型,并通过计算Kendall秩相关系数和Friedman统计量验证了该方法残留噪声的高斯性和独立性。

关键词: 杂波抑制, 小波变换, 隐马尔可夫树模型, Kendall秩相关系数, Friedman统计量

Abstract:

Aiming at the detection of infrared faint blob-shaped moving target under complicated background, this paper presents an image clutter suppression method based on wavelet domain Hidden Markov Tree(HMT). A wavelet-domain HMT model is used to accurately capture the dependencies across low frequency scales. It uses Bayesian criterion to estimate image background wavelet coefficients, refers to clutter suppression model to get clutter suppression image signal noise adding model. Gaussianity and independency of residual noise are also verified by using Kendall rank correlation coefficient and Friedman statistic.

Key words: clutter suppression, wavelet transform, Hidden Markov Tree(HMT) model, Kendall rank correlation coefficient, Friedman statistic

中图分类号: