高斯混合模型自适应微光图像增强

陈莹; 朱明; 刘剑; 李兆泽

doi:10.3788/YJYXS20153002.0300

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高斯混合模型自适应微光图像增强

成像技术与图像处理 | 更新时间：2020-08-12

- 高斯混合模型自适应微光图像增强
- Automatic low light level image enhancement using Gaussian mixture modeling
- 液晶与显示 2015年30卷第2期页码：300-309
- 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. 中国科学院大学北京,510640
  3. 总装备部沈阳军事代表局驻长春地区军事代表室,吉林长春,130033
- 作者简介：
  
  [ "陈莹(1989-),女,辽宁盘锦人,硕士研究生,研究方向为数字图像处理、图像增强。E-mail:chenying_0305@163.com" ]
  [ "朱明（1964-），男，江西南昌人，研究员，博士生导师，主要从数字图像处理、电视跟踪、自动目标识别等技术的研究。E-mail：zhu_mingca@163.com" ]
  [ "刘剑(1973-),男,吉林长春人,高级工程师,从事图像处理研究" ]
  [ "李兆泽(1980-),男,辽宁庄河人,主要从事信号处理等方面的研究。E-mail:lzzsp@aliyun.com" ]
- 基金信息：
  
  国家自然科学基金(No.61203242)()
- DOI：10.3788/YJYXS20153002.0300
  中图分类号： TP391.4
- 收稿日期：2014-04-23，
  
  修回日期：2014-06-11，
  
  网络出版日期：2014-06-11，
  
  纸质出版日期：2015-04-05
- 稿件说明：
移动端阅览
陈莹, 朱明, 刘剑, 李兆泽. 高斯混合模型自适应微光图像增强[J]. 液晶与显示, 2015,30(2): 300-309

CHEN Ying, ZHU Ming, LIU Jian, LI Zhao-ze. Automatic low light level image enhancement using Gaussian mixture modeling[J]. , 2015,30(2): 300-309
陈莹, 朱明, 刘剑, 李兆泽. 高斯混合模型自适应微光图像增强[J]. 液晶与显示, 2015,30(2): 300-309 DOI： 10.3788/YJYXS20153002.0300.

CHEN Ying, ZHU Ming, LIU Jian, LI Zhao-ze. Automatic low light level image enhancement using Gaussian mixture modeling[J]. , 2015,30(2): 300-309 DOI： 10.3788/YJYXS20153002.0300.

摘要

为了改善微光情况下可见光图像传感器输出图像的质量

提出了一种基于高斯混合模型的自适应微光图像增强算法。对图像的直方图进行混合高斯建模

应用改进的期望最大化算法对直方图拟合

从而获取高斯混合模型的最优参数

然后根据各个聚类的交点将直方图分区

最后确定输出图像所属聚类的映射关系

同时应用保持最大熵方法逼近人类视觉特性映射函数得到最终的增强图像。实验结果表明

此图像增强模型能自适应确定最佳聚类个数

提高直方图拟合的运算速度

一帧图像平均处理时间为0.37 s

在相关信息熵和纹理信息等的客观评价中

增强结果明显优于传统方法

有效地提高了微光图像的对比度

同时保持了图像的细节。

Abstract

In order to improve the output quality of visible light sensor in low-light environment

an adaptive image enhancement algorithm using Gaussian mixture modeling is proposed in this paper. The histogram of image is modeled with Gaussian mixture modeling and the improved EM algorithm is used to fit the histogram and get the best parameters. Then

the histogram is separated into sub-histograms based on the intersections of Gaussian components. Finally

the mapping is achieved according to the parameters of output image

and the final enhanced image is obtained by the maximum entropy preserving method which tends to the characteristics of human visual. The experimental results show that the algorithm can determine the optimal number of clusters adaptively and improve the speed of the histogram fitting which costs 0.37 s averagely. Comparing with traditional methods

the enhancement result is superior in terms of objective evaluations of related information entropy and texture information. It can improve the contrast of the low light level image and maintain the details.

关键词

Keywords

references

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