Detecting Algorithm of Infrared Small Dim Targets Based on Complexity and Orientation Gradient

WANG Tian; LIU Wei-ning; SUN Hai-jiang; HAN Guang-liang

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Detecting Algorithm of Infrared Small Dim Targets Based on Complexity and Orientation Gradient

更新时间：2020-08-12

- Detecting Algorithm of Infrared Small Dim Targets Based on Complexity and Orientation Gradient
- Chinese Journal of Liquid Crystals and Displays Vol. 27, Issue 5, Pages: 692-696(2012)
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所,吉林长春,中国,130033
  2. 中国科学院研究生院北京,100049
- 作者简介：
- 基金信息：
- DOI：
  CLC： V529.1
- Received：06 March 2012，
  
  Revised：16 May 2012，
  
  Published：15 October 2012
- 稿件说明：
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王田, 刘伟宁, 孙海江, 韩广良. 基于复杂度和方向梯度的红外弱小目标检测方法[J]. 液晶与显示, 2012,(5): 692-696

WANG Tian, LIU Wei-ning, SUN Hai-jiang, HAN Guang-liang. Detecting Algorithm of Infrared Small Dim Targets Based on Complexity and Orientation Gradient[J]. , 2012,(5): 692-696
王田, 刘伟宁, 孙海江, 韩广良. 基于复杂度和方向梯度的红外弱小目标检测方法[J]. 液晶与显示, 2012,(5): 692-696 DOI：

WANG Tian, LIU Wei-ning, SUN Hai-jiang, HAN Guang-liang. Detecting Algorithm of Infrared Small Dim Targets Based on Complexity and Orientation Gradient[J]. , 2012,(5): 692-696 DOI：

摘要

为解决天空背景下红外弱小目标检测问题

提出了一种基于图像复杂度和方向梯度的检测方法。利用信息熵对图像复杂度进行描述

引入了图像方差和像素局部变化率对信息熵进行加权

使云内部和云边界区域得到抑制。以复杂度为描述对象

建立多级多方向梯度模型

在背景局部复杂度高于目标复杂度的情况下

仍能够有效分割出目标。实验证明

该方法能够在复杂云背景情况下检测出弱小目标。

Abstract

In order to solve the problem of detecting small dim targets in infrared images with the complicated air background

an improved algorithm based on image complexity and orientation gradient is put forward. The information entropy reflects the complexity of the image. The weighted information entropy with the variance and the change rate of local pixels suppresses both the inside and the edge of clouds. Since the image of the complexity is obtained

targets can be extracted by the means of multi-degree and multi-orientation gradient under the circumstance that the complexity of the background is higher than that of the targets. Experiments indicate that the algorithm can detect small dim targets effectively with complicated cloud background.

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Keywords

references

秦翰林,周慧鑫,刘群昌,等.采用多尺度隐式马尔可夫模型的红外图像背景抑制 [J]. 光学精密工程, 2011,19(8):1950-1956.

王博. 红外序列图像中运动弱小目标时域检测方法.西安:西安电子科技大学,2010.

张朝亮,汉江红,姜春良.基于SIFT和加权信息熵的红外小目标检测 [J]. 光电工程, 2010,37(11):19-25.

Jiang Feng, Jin Minhui, Song Liquan. Small infrared target detection algorithm based on mathematical morphology //SPIE International Symposium on Photoelectronic Detection and Imaging,Beijing,China:SPIE,2011:81934L(1-7).

李欣,赵亦工,郭伟.基于复杂度的自适应门限弱小目标检测方法 [J]. 光子学报,2009, 38(8):2144-2149.

靳永亮,王延杰,丁南南,等.改进的红外弱小目标检测方法 [J]. 液晶与显示, 2011, 26(4):555-560.

Chen Jinling, Zhang Jiming, Deng Linming,et al. Detection of weak and small infrared targets under complicated background //SPIE 5th International Symposium on Advanced Optical Manufacturing and Testing Technologies, Dalian, China: SPIE, 2010:765823(1-7).

姚杰.复杂背景中红外小目标的检测算法研究.南京:南京航空航天大学,2010.

高振宇,杨晓梅,龚剑明.图像复杂度描述方法研究 [J]. 中国图像图形学报, 2010, 15(1):129-135.

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