Contrast Enhancement of RGB Color Images by Histogram Equalization of Color Vectors' Intensities

被引:6
作者
Garcia-Lamont, Farid [1 ]
Cervantes, Jair [1 ]
Lopez-Chau, Asdrubal [2 ]
Ruiz, Sergio [1 ]
机构
[1] Univ Autonoma Estado Mexico, Ctr Univ UAEM Texcoco, Texcoco, Estado De Mexic, Mexico
[2] Univ Autonoma Estado Mexico, Ctr Univ UAEM Zumpango, Zumpango, Estado De Mexic, Mexico
来源
INTELLIGENT COMPUTING METHODOLOGIES, ICIC 2018, PT III | 2018年 / 10956卷
关键词
Color characterization; Histogram equalization; RGB images; BRIGHTNESS;
D O I
10.1007/978-3-319-95957-3_47
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The histogram equalization (HE) is a technique developed for image contrast enhancement of grayscale images. For RGB (Red, Green, Blue) color images, the HE is usually applied in the color channels separately; due to correlation between the color channels, the chromaticity of colors is modified. In order to overcome this problem, the colors of the image are mapped to different color spaces where the chromaticity and the intensity of colors are decoupled; then, the HE is applied in the intensity channel. Mapping colors between different color spaces may involve a huge computational load, because the mathematical operations are not linear. In this paper we present a proposal for contrast enhancement of RGB color images, without mapping the colors to different color spaces, where the HE is applied to the intensities of the color vectors. We show that the images obtained with our proposal are very similar to the images processed in the HSV (Hue, Saturation, Value) and L*a*b* color spaces.
引用
收藏
页码:443 / 455
页数:13
相关论文
共 19 条
[1]   Medical Image Contrast Enhancement using Range Limited Weighted Histogram Equalization [J].
Agarwal, Monika ;
Mahajan, Rashima .
6TH INTERNATIONAL CONFERENCE ON SMART COMPUTING AND COMMUNICATIONS, 2018, 125 :149-156
[2]  
[Anonymous], 2006, Digital Image Processing
[3]   Automatic system for improving underwater image contrast and color through recursive adaptive histogram modification [J].
Ghani, Ahmad Shahrizan Abdul ;
Isa, Nor Ashidi Mat .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2017, 141 :181-195
[4]   New contrast enhancement approach for dark images with non-uniform illumination [J].
Gupta, Bhupendra ;
Agarwal, Tarun Kumar .
COMPUTERS & ELECTRICAL ENGINEERING, 2018, 70 :616-630
[5]   An evolution of image source camera attribution approaches [J].
Jahanirad, Mehdi ;
Wahab, Ainuddin Wahid Abdul ;
Anuar, Nor Badrul .
FORENSIC SCIENCE INTERNATIONAL, 2016, 262 :242-275
[6]   Learning Coupled Classifiers with RGB images for RGB-D object recognition [J].
Li, Xiao ;
Fang, Min ;
Zhang, Ju-Jie ;
Wu, Jinqiao .
PATTERN RECOGNITION, 2017, 61 :433-446
[7]   An adaptive RGB colour enhancement formulation for logarithmic image processing-based algorithms [J].
Nnolim, U. A. .
OPTIK, 2018, 154 :192-215
[8]   Segmentation of color images using a two-stage self-organizing network [J].
Ong, SH ;
Yeo, NC ;
Lee, KH ;
Venkatesh, YV ;
Cao, DM .
IMAGE AND VISION COMPUTING, 2002, 20 (04) :279-289
[9]   A multilevel color image segmentation technique based on cuckoo search algorithm and energy curve [J].
Pare, S. ;
Kumar, A. ;
Bajaj, V. ;
Singh, G. K. .
APPLIED SOFT COMPUTING, 2016, 47 :76-102
[10]   Perceptually uniform color spaces for color texture analysis: An empirical evaluation [J].
Paschos, G .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2001, 10 (06) :932-937