506 IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 20, NO. 2, FEBRUARY 2011
A Novel 3-D Color Histogram Equalization Method
With Uniform 1-D Gray Scale Histogram
Ji-Hee Han, Sejung Yang, and Byung-Uk Lee, Member, IEEE
Abstract—The majority of color histogram equalization methods
do not yield uniform histogram in gray scale. After converting a
color histogram equalized image into gray scale, the contrast of the
converted image is worse than that of an 1-D gray scale histogram
equalized image. We propose a novel 3-D color histogram equal-
ization method that produces uniform distribution in gray scale
histogram by defining a new cumulative probability density func-
tion in 3-D color space. Test results with natural and synthetic im-
ages are presented to compare and analyze various color histogram
equalization algorithms based upon 3-D color histograms. We also
present theoretical analysis for nonideal performance of existing
methods.
Index Terms—Color image enhancement, gray scale histogram
equalization, 3-D color histogram equalization.
I. INTRODUCTION
T
HE USAGE of digital images has rapidly increased with
growing public consumption of entertainment and com-
munication appliances, such as digital TV’s, digital cameras,
scanners, mobile phone cameras, and personal media players.
The expectation of a higher image quality prompts researchers
to develop cutting-edge techniques for image enhancement.
Histogram equalization has been one of the most widely used
techniques due to its effectiveness and simplicity in contrast
enhancement. Therefore, histogram equalization has become
embedded in most consumer digital cameras. Histogram equal-
ization modifies the pixel values in such a way that the intensity
histogram of the resulting image becomes uniform. The output
image then makes use of all the possible brightness values,
thus, resulting in enhanced contrast [1].
First, we will review previous studies on color histogram
equalization methods based upon 3-D histograms. The his-
togram equalization of a color image is more complex than 1-D
equalization due to multidimensional nature of color signal. A
typical color image has three color components: red (R), green
Manuscript received August 21, 2009; revised January 18, 2010 and June 02,
2010; accepted August 03, 2010. Date of publication August 26, 2010; date
of current version January 14, 2011. This work was supported in part by the
Ministry of Knowledge Economy (MKE), Korea Industrial Technology Foun-
dation (KOTEF) through the Human Resource Training Project for Strategic
Technology, the Acceleration Research Program of the Ministry of Education,
Science and Technology of Korea and the Korea Science and Engineering Foun-
dation, and Ewha W. University under Grant 2008-1806-1. The associate editor
coordinating the review of this manuscript and approving it for publication was
Prof. Jesus Malo.
The authors are with the Electronics Engineering Department, Ewha
W. University, Seoul 120-750, Korea (e-mail: jiheehan87227@gmail.com;
sejungyang@gmail.com; bulee@ewha.ac.kr).
Color versions of one or more of the figures in this paper are available online
at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TIP.2010.2068555
(G), and blue (B). Trahanias and Vanetsanopoulos [2] proposed
to use a 3-D color histogram instead of independently applying
1-D histogram equalization to each R, G, and B channel. They
defined ideal output probability density function (pdf) to be
uniform in the color space, and the cumulative distribution
function (cdf) to be accumulation of pdfs within a box of size
in 3-D color space. Although uniform pdf in gray
scale dramatically enhances contrast of the images, uniform pdf
in 3-D color space does not result in uniform pdf in luminance
domain with the box shape cdf. Most of the natural images that
are equalized using this method show higher concentration of
bright pixels. We analyze the theory behind this less-than-ideal
performance of contrast enhancement in Section II. Menotti
et
al. [3] partially overcame this lack of contrast enhancement by
defining a new cdf that is a multiplication of the marginal cdfs
of each color channel. However, this heuristic method does not
work properly when color correlation is low. Other approaches
for multidimensional histogram equalization include weighted
1-D marginal histogram equalization [4], and iterative matching
of 1-D marginal pdf [5].
This work elucidates the reason behind nonideal performance
of 3-D color histogram equalization algorithms, and proposes
a new definition of cdf in RGB color space that will result in
uniform luminance distribution after equalization. Since gray
scale histogram equalization is a powerful and effective tool for
contrast enhancement, achieving uniform luminance pdf is an
important feature for image enhancement.
There are many other color histogram equalization methods
that are not directly related to the 3-D histogram. Mlsna and
Rodriguez [6] introduced a histogram explosion method in 3-D
color space. This method expands the color space of an image
by equalizing 1-D histogram along a line from a central point in
color space to the R, G, and B boundary points. The same author
also applied this method in CIELUV color space [7]. Pitas pro-
posed a multichannel histogram equalization method [8] using
conditional probability density functions in HSI color space, and
Lucchese suggested an equalization in x-y color space [9].
Several new approaches are based upon optimization. Kim
and Yang interpolated the discrete pdf with Gaussian functions
and applied nonlinear optimization [10]. Morovic and Sun
found 3-D color histogram transformation using linear pro-
gramming [11]. Arici et al. defined a cost function composed
of image change, histogram deviation from the target, and
histogram smoothness [12]. Chen et al. proposed gray-level
grouping (GLG) that groups adjacent low values of histogram
bins and then redistributes these groups iteratively [13]. Most
of the histogram equalization algorithms use a histogram of
the whole image. However, the use of an adaptive or local his-
togram enhances each region with different mapping depending
1057-7149/$26.00 © 2011 IEEE
下载后可阅读完整内容，剩余6页未读，立即下载
jianminfly
- 粉丝: 15
- 资源: 7
我的内容管理
展开
最新资源
- 多传感器数据融合手册：国外原版技术指南
- MyEclipse快捷键大全，提升编程效率
- 从零开始的编程学习：Linux汇编语言入门
- EJB3.0实例教程：从入门到精通
- 深入理解jQuery源码：解析与分析
- MMC-1电机控制ASSP芯片用户手册
- HS1101相对湿度传感器技术规格与应用
- Shell基础入门：权限管理与常用命令详解
- 2003年全国大学生电子设计竞赛：电压控制LC振荡器与宽带放大器
- Android手机用户代理(User Agent)详解与示例
- Java代码规范：提升软件质量和团队协作的关键
- 浙江电信移动业务接入与ISAG接口实战指南
- 电子密码锁设计：安全便捷的新型锁具
- NavTech SDAL格式规范1.7版：车辆导航数据标准
- Surfer8中文入门手册：绘制等高线与克服语言障碍
- 排序算法全解析：冒泡、选择、插入、Shell、快速排序
资源上传下载、课程学习等过程中有任何疑问或建议，欢迎提出宝贵意见哦~我们会及时处理！
点击此处反馈
