电子学报 ›› 2018, Vol. 46 ›› Issue (10): 2367-2375.DOI: 10.3969/j.issn.0372-2112.2018.10.009

• 学术论文 • 上一篇    下一篇

基于直方图均衡化图像增强的两种改进方法

董丽丽, 丁畅, 许文海   

  1. 大连海事大学信息科学技术学院, 辽宁大连 116026
  • 收稿日期:2017-09-15 修回日期:2018-03-26 出版日期:2018-10-25
    • 通讯作者:
    • 丁畅
    • 作者简介:
    • 董丽丽,女,1980年11月出生于黑龙江七台河.2008年于哈尔滨工业大学获得博士学位.现为大连海事大学副教授、硕士生导师,主要研究方向为图像处理、光电信息与光电检测.E-mail:1147776326@qq.com;许文海,男,1956年4月出生于吉林扶余.1991年于哈尔滨工业大学获得博士学位,1993年于东京工业大学获得博士学位.现为大连海事大学教授、博士生导师,主要研究方向为图像处理、光电信息与光电检测.E-mail:xuwenhai@dlmu.edu.cn
    • 基金资助:
    • 国家科技支撑计划 (No.2014BAB12B03); 国家自然科学基金 (No.61501077); 中央高校基本科研业务费专项资金 (No.3132016351,No.3132018189)

Two Improved Methods Based on Histogram Equalization for Image Enhancement

DONG Li-li, DING Chang, XU Wen-hai   

  1. School of Information Science and Technology, Dalian Maritime University, Dalian, Liaoning 116026, China
  • Received:2017-09-15 Revised:2018-03-26 Online:2018-10-25 Published:2018-10-25

摘要: 直方图均衡化(Histogram Equalization,HE)是图像增强领域中基础性很强的方法,对其研究和改进工作至关重要.首先,本文分析了经典HE算法的缺点,也概括了五类基于直方图均衡化的图像增强技术,然后针对HE经典算法的缺点提出了两种改进方法,分别引入了直方图动态削峰技术和边缘信息融合技术,最后选取曝光不足和过曝光的两类图像验证算法的性能,采取了有效的图像客观质量评价指标对实验结果做出评价.结合主客观图像质量评价可以看出,本文提出的算法具有增强效果好、输入参数少等特点.

关键词: 图像增强, 直方图均衡化, 子直方图均衡化, 极大值搜索, 动态削峰, 边缘锐化, 信息融合

Abstract: HE (Histogram Equalization) is a fundamental method in the field of image enhancement,the research and improvement about which is very significant.First,this paper analyzes the disadvantages of the classical HE algorithm and summarizes five kinds of image enhancement techniques based on HE.Then,two kinds of improved methods are proposed aiming at the disadvantages of the classical HE algorithm,the techniques of the peak clipping and the edge information fusion are introduced.Finally,underexposure and overexposure images are selected to verify the algorithms' properties,the standard of efficient image objective quality assessment is selected to evaluate the experimental results.The assessment of image subjective and objective quality shows the algorithms this paper proposes have the characteristics of better results,less input parameters and so on.

Key words: image enhancement, histogram equalization, sub-histogram equalization, maximum value searching, peak clipping dynamically, edge sharpening, information fusion

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