[1]曹美,程亚玲,盛惠兴,等.改进的直方图均衡化和NSCT变换的红外图像增强[J].应用科技,2016,43(02):24-27.[doi:10.11991/yykj.201506024]
 CAO Mei,CHENG Yaling,SHENG Huixing,et al.Application of improved histogram equalization and NSCT transform algorithm in infrared image enhancement[J].Applied science and technology,2016,43(02):24-27.[doi:10.11991/yykj.201506024]
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改进的直方图均衡化和NSCT变换的红外图像增强(/HTML)
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《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
第43卷
期数:
2016年02期
页码:
24-27
栏目:
自动化技术
出版日期:
2016-04-05

文章信息/Info

Title:
Application of improved histogram equalization and NSCT transform algorithm in infrared image enhancement
作者:
曹美1 程亚玲1 盛惠兴12 仇春春1 俞楷1
1. 河海大学物联网工程学院, 江苏常州 213022;
2. 常州市传感网与环境感知重点实验室, 江苏常州 213022
Author(s):
CAO Mei1 CHENG Yaling1 SHENG Huixing12 QIU Chunchun1 YU Kai1
1. College of Internet of Things Engineering, Hohai University, Changzhou 213022, China;
2. Changzhou Key Laboratory of Sensor Networks and Environmental Sensing, Changzhou 213022, China
关键词:
图像处理红外图像增强NSCT直方图均衡化非线性增益函数
Keywords:
image processinginfrared image enhancementNSCThistogram quantizationnonlinear gain function.
分类号:
TP394.1
DOI:
10.11991/yykj.201506024
文献标志码:
A
摘要:
针对红外图像对比度低、噪声大等特点,提出一种基于改进的直方图均衡化和NSCT变换(nonsub sampled contourlet transform)相结合的的红外图像增强方法。首先对红外图像进行NSCT变换,得到多尺度多方向的高频子带系数和一个低频子带系数。高频子带首先采用自适应降噪函数降低高频系数的噪声同时很好地保护边缘信息,然后采用非线性增益函数调整高频分量;在低频分量上采用改进的直方图均衡化方法进行增强。最后经NSCT逆变换得到增强图像。实验结果表明,提出的算法可以有效地提高红外图像的对比度,抑制图像的噪声,增强图像对比度,突出图像的边缘轮廓信息。
Abstract:
Considering the problems of low contrast and large noise of infrared images, an infrared image enhancement method is presented in this paper, which is based on the integrated improved histogram equalization and nonsub sampled contourlet transform(NSCT) transform. Firstly, the high-frequency sub-band coefficients and low-frequency sub-band coefficient in different scales and different directions were obtained by image decomposition using the nonsubsampled NSCT transform. In the high-frequency sub-band, the adaptive method was used to reduce noise and protect the details of image edge. Then, the nonlinear gain function was applied to enhance the image’s high-frequency. The improved histogram quantization was used to enhance the low-frequency. Finally, the enhanced infrared image was obtained by transforming these changed coefficients back to the spatial domain. Experimental results demonstrate that the proposed algorithm can effectively improve the contrast of the infrared image, obviously reduce noise of the image, and highlight details of the image edge.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2015-06-16;改回日期:。
基金项目:国家自然科学基金项目(60972101,41306089);江苏省自然科学基金项目(BK20130240).
作者简介:曹美(1989-),男,硕士研究生;盛惠兴(1961-),男,副教授.
通讯作者:盛惠兴,E-mail:Sheng_hx@139.com.
更新日期/Last Update: 2016-04-07