Enhancement algorithm for color images based on co-occurrence matrix

doi:10.3724/SP.J.1087.2012.02573

Journal of Computer Applications ›› 2012, Vol. 32 ›› Issue (09): 2573-2575.DOI: 10.3724/SP.J.1087.2012.02573

• Graphics and image technology • Previous Articles Next Articles

Enhancement algorithm for color images based on co-occurrence matrix

YANG Bing-qing^1*,TIAN Xiao-ping²,WU Cheng-mao²

1.School of Communication and Information Engineering,Xi'an University of Posts and Telecommunications,Xi'an Shaanxi 710061,China;
2.School of Electronic Engineering,Xi'an University of Posts and Telecommunications,Xi'an Shaanxi 710061,China

Received:2012-03-23 Revised:2012-05-11 Online:2012-09-01 Published:2012-09-01

基于共生矩阵的彩色图像增强算法

杨冰清^1*,田小平²,吴成茂²

1.西安邮电大学通信与信息工程学院,西安710061;
2.西安邮电大学电子工程学院,西安710061

通讯作者: 杨冰清
作者简介:杨冰清(1986-),女,陕西韩城人,硕士研究生,主要研究方向:图像与视频通信; 田小平(1963-),男,陕西渭南人,教授,主要研究方向:图像处理、图像通信; 吴成茂(1968-),男,四川仪陇人,高级工程师,主要研究方向:图像处理。
基金资助:
陕西省自然科学基金资助项目(2009JM8004);陕西省教育厅科技计划项目(2009JK730,2010JK816)

Abstract

Abstract: Considering that the traditional method of histogram equalization can make the image produce unnatural enhanced results, a new color image contrast enhancement approach was proposed, which equalized color components by dependently using co-occurrence matrix. Firstly, gray correlative characteristics of pixels were combined in 3×3 neighborhoods to construct a co-occurrence matrix. Secondly, the method of co-occurrence histogram equalization was applied to luminance component only and the chrominance components were preserved. Finally, dark channel prior was used to solve the problem mentioned above to obtain the ideal image. Comparing the proposed enhanced algorithm with the other three typical enhancement algorithms, it is shown that the proposed algorithm not only considers the whole and local image information, but also deals with the histogram spikes and halo effect. The experimental results demonstrate that the proposed method can enhance the color images effectively.

Key words: image enhancement, co-occurrence matrix, dark channel prior, halo effect, definition

摘要： 针对传统的直方图均衡化会使图像过分增强,产生不自然的现象,提出一种利用二维共生矩阵的彩色图像增强算法。首先,利用相邻3×3像素之间的相关性来构造共生矩阵;然后,对彩色图像中的亮度分量进行共生直方图均衡;最后,将增强后的图像进行暗原色处理得到比较理想的图像。从所提增强算法与其他3种典型的增强算法的比较可看出:所提算法不仅考虑了图像的全局与局部信息,而且降低了直方图峰值和光环效应,提高了图像对比度,有较好的增强效果。

关键词: 图像增强, 共生矩阵, 暗原色先验, 光环效应, 清晰度

CLC Number:

TP317.4

YANG Bing-qing TIAN Xiao-ping WU Cheng-mao. Enhancement algorithm for color images based on co-occurrence matrix[J]. Journal of Computer Applications, 2012, 32(09): 2573-2575.

杨冰清田小平吴成茂. 基于共生矩阵的彩色图像增强算法[J]. 计算机应用, 2012, 32(09): 2573-2575.

References

[1]CHENG H D,SHI X J.A simple and effective histogram equalization approach to image enhancement[J].Digital Signal Processing 2004,14(2):158-170.
[2]张懿,刘旭,李海峰.自适应图像直方图均衡化算法[J].浙江大学学报:工学版,2007,41(4):630-633.
[3]徐同莹,彭定明,王卫星.改进的直方图均衡化算法[J].网络信息技术,2006,25(7):58-59.
[4]张勇,张剑,欧翼,等.基于随机分布的局部直方图均衡算法[J].现代科学仪器,2011,28(3):11-13.
[5]余小宝,莫玉龙.具有视觉特性的局部直方图均衡增强算法[J].上海大学学报:自然科学版,2003,9(2):109-112.
[6]KIM Y T.Contrast enhancement using brightness preserving bi-histogram Equalization[J].IEEE Transactions on Consum Electron,1997,43(1):1-8.
[7]赵晓丽,孙宪坤.基于视觉特性的彩色图像增强算法研究[J].计算机工程与设计,2007,24(6):204-207.
[8]江巨浪,张佑生,薛峰,等.保持图像亮度的局部直方图均衡算法[J].电子学报,2006,34(5):861-866.
[9]KIM J-Y,KIM L-S,HWANG S-H.An advanced contrast enhancement using partially overlapped sub-block histogram equalization[J].IEEE Transactions on Circuits and Systems for Video Technology,2001,11(4):474-484.
[10]翟东清,吴陈,曾庆军.保持亮度的多峰直方图均衡算法[J].计算机仿真,2009,26(1):222-224.
[11]HE KAIMING,SUN JIAN,TANG XIAO.Single image haze removal using dark channel prior[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2011,33(12):2341-2353.
[12]BARALDI A,PARMIGGIANI F.An investigation of the textural haracteristics associated with GLCM matrix statistical parameters[J].IEEE Transactions on Geoscience and Remote Sensing,1995,33(2):293-304.
[13]吴成茂.可调直方图均衡化的正则解释及其改进[J].电子学报,2011,39(6):1279-1281.
[14]ESKICIOGLU A M,FISHER P S.Image quality measures and their performance[J].IEEE Transactions on Communications,1995,43(12):2959-5965.
[15]李奇,冯华君,徐之海,等.数字图像清晰度评价函数研究[J].光子学报,2001,31(6):736-738.
[16]查世华,王旭,张舞杰,等.一种改进的数字图像清晰度评价函数研究[J].微计算机信息,2007,23(12):279-280.

Enhancement algorithm for color images based on co-occurrence matrix

基于共生矩阵的彩色图像增强算法

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

[1]	NIU Kangli, CHEN Yuzhang, SHEN Junfeng, ZENG Zhangfan, PAN Yongcai, WANG Yichong. Dual-channel night vision image restoration method based on deep learning [J]. Journal of Computer Applications, 2021, 41(6): 1775-1784.
[2]	YANG Mengmeng, ZHANG Aihua. Fractal image compression based on gray-level co-occurrence matrix and simultaneous orthogonal matching pursuit [J]. Journal of Computer Applications, 2021, 41(5): 1445-1449.
[3]	HE Jiean, WU Xiaohong, HE Xiaohai, HU Jianrong, QIN Linbo. Imperfect wheat kernel recognition combined with image enhancement and conventional neural network [J]. Journal of Computer Applications, 2021, 41(3): 911-916.
[4]	YANG Jiayi, CHEN Yong. Design and implementation of adaptive compensation enhancement for low-light video images [J]. Journal of Computer Applications, 2020, 40(8): 2372-2377.
[5]	YU Tianhe, LI Yuzuo, LAN Chaofeng. Bionic image enhancement algorithm based on top-bottom hat transformation [J]. Journal of Computer Applications, 2020, 40(5): 1440-1445.
[6]	WANG Ruiyao, YUE Xueting, ZHOU Zhiqing, GENG Zexun. Adaptive enhancement algorithm of low illumination image based on maximum difference image decision [J]. Journal of Computer Applications, 2020, 40(4): 1164-1170.
[7]	WANG Yunfei, WANG Yuanyu. MP-CGAN: night single image dehazing algorithm based on Msmall-Patch training [J]. Journal of Computer Applications, 2020, 40(3): 865-871.
[8]	ZHANG Yubo, ZHANG Yadong, ZHANG Bin. Desktop dust detection algorithm based on gray gradient co-occurrence matrix [J]. Journal of Computer Applications, 2019, 39(8): 2414-2419.
[9]	SIMA Ziling, HU Feng. Low-light image enhancement method based on simulating multi-exposure fusion [J]. Journal of Computer Applications, 2019, 39(6): 1804-1809.
[10]	CHENG Yu, DENG Dexiang, YAN Jia, FAN Ci'en. Weakly illuminated image enhancement algorithm based on convolutional neural network [J]. Journal of Computer Applications, 2019, 39(4): 1162-1169.
[11]	LOU Mengying, YUAN Lisha, LIU Yaqin, WAN Xuemei, YANG Feng. Palm vein enhancement method based on adaptive fusion [J]. Journal of Computer Applications, 2019, 39(4): 1176-1182.
[12]	ZHOU Chong, LIU Huan, ZHAO Ailing, ZHANG Pengcheng, LIU Yi, GUI Zhiguo. Industrial X-ray image enhancement algorithm based on gradient field [J]. Journal of Computer Applications, 2019, 39(10): 3088-3092.
[13]	LI Hong, WANG Ruiyao, GENG Zexun, HU Haifeng. Low-illumination image enhancement algorithm based on multi-scale gradient domain guided filtering [J]. Journal of Computer Applications, 2019, 39(10): 3046-3052.
[14]	LEI Sijia, ZHAO Fengqun. Foggy image enhancement based on adaptive Riesz fractional differential [J]. Journal of Computer Applications, 2018, 38(5): 1427-1431.
[15]	LI Lin, ZHANG Tao. Zenithal pedestrian detection algorithm based on improved aggregate channel features and gray-level co-occurrence matrix [J]. Journal of Computer Applications, 2018, 38(12): 3367-3371.