最大差值图决策的低照度图像自适应增强算法

doi:10.11772/j.issn.1001-9081.2019091541

计算机应用 ›› 2020, Vol. 40 ›› Issue (4): 1164-1170.DOI: 10.11772/j.issn.1001-9081.2019091541

• 虚拟现实与多媒体计算 • 上一篇下一篇

最大差值图决策的低照度图像自适应增强算法

王瑞尧, 岳雪亭, 周志青, 耿则勋

平顶山学院信息工程学院, 河南平顶山 467000

收稿日期:2019-09-05 修回日期:2019-10-28 出版日期:2020-04-10 发布日期:2019-11-11
通讯作者: 王瑞尧
作者简介:王瑞尧(1989-),男,河南平顶山人,讲师,硕士,主要研究方向:数字图像处理;岳雪亭(1989-),女,河南平顶山人,助教,硕士,主要研究方向:智能信息处理;周志青(1978-),男,浙江台州人,讲师,硕士,主要研究方向:智能信息处理;耿则勋(1958-),男,河南郑州人,教授,博士,主要研究方向:遥感图像处理。
基金资助:
河南省科技计划项目（162102310248）。

Adaptive enhancement algorithm of low illumination image based on maximum difference image decision

WANG Ruiyao, YUE Xueting, ZHOU Zhiqing, GENG Zexun

School of Information Engineering, Pingdingshan University, Pingdingshan Henan 467000, China

Received:2019-09-05 Revised:2019-10-28 Online:2020-04-10 Published:2019-11-11
Supported by:
This work is partially supported by the Science and Technology Project of Henan Province(162102310248).

摘要/Abstract

摘要： 应用于光照分布不均的低照度图像，传统的图像增强算法会出现色彩失真、亮区过度增强等问题，因此提出一种最大差值图决策的低照度图像自适应增强算法。首先，提出最大差值图的概念，通过最大差值图粗略估计出初始光照分量；然后，提出交替引导滤波的算法，利用交替引导滤波对初始光照分量进行校正，实现光照分量的准确估计；最后，设计了图像亮度自适应的伽马变换，能够根据获取的光照分量自适应调整伽马变换参数，从而在增强图像的同时消除光照不均带来的影响。实验结果表明，增强后的图像有效消除了光照分布不均带来的影响，图像亮度、对比度、细节表现能力和色彩保真度都得到了明显提升，平均梯度提升了1倍以上，信息熵提升了14%以上。由于提出的算法对光照分量估计准确，自适应伽马变换针对低照度图像进行了优化，因此，对于夜间等弱光源条件下的彩色图像具有十分有效的增强效果。

关键词: 低照度图像, 图像增强, 最大差值图像, 交替引导滤波, 自适应伽马变换

Abstract: When applying traditional image enhancement algorithm to low illumination images with uneven illumination distribution,it is easy to produce color distortion and over enhancement of bright areas. To resolve theses problems,an adaptive enhancement algorithm of low illumination image based on maximum difference image was proposed. Firstly,the concept of maximum difference image was proposed,and the initial illumination component was roughly estimated by the maximum difference image. Secondly,the method of alternating guided filtering was proposed,which was used to correct the initial illumination component,so as to realize the accurate estimation of illumination component. Finally,the Gamma transform was designed for image brightness adaptivity,which was able to adaptively adjust the Gamma transform parameters according to the acquired illumination components,thus,the influence of uneven illumination was eliminated while enhancing the image. Experimental results show that the enhanced image effectively eliminates the influence of uneven illumination distribution,the brightness,contrast,detail performance and color fidelity of the image are significantly improved,the average gradient increases by more than one time,and the information entropy increases by more than 14%. Because the proposed algorithm estimates the light component accurately,and the adaptive Gamma transform is optimized for low illumination images,so that the proposed algorithm has very effective enhancement effect for color images under weak light conditions like night.

Key words: low illumination image, image enhancement, maximum difference image, alternating guided filtering, adaptive Gamma transform

中图分类号:

TP391.41

王瑞尧, 岳雪亭, 周志青, 耿则勋. 最大差值图决策的低照度图像自适应增强算法[J]. 计算机应用, 2020, 40(4): 1164-1170.

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.

参考文献

[1] IBRAHIM H, KONG N S P. Brightness preserving dynamic histogram equalization for image contrast enhancement[J]. IEEE Transactions on Consumer Electronics,2007,53(4):1752-1758.
[2] REZA A M. Realization of the Contrast Limited Adaptive Histogram Equalization (CLAHE) for real-time image enhancement[J]. Journal of VLSI Signal Processing Systems for Signal,Image and Video Technology,2004,38(1):35-44.
[3] 武英. 保持图像亮度的改进双直方图均衡算法[J]. 计算机应用,2010,30(6):1632-1634. (WU Y. Improved brightness preserving bi-histogram equalization algorithm[J]. Computer Application,2010,30(6):1632-1634.)
[4] 张毅, 李杨, 谢陈磊, 等. 基于变异系数反馈调整的自适应双平台直方图均衡化[J]. 红外技术,2019,41(3):262-267.(ZHANG Y, LI Y, XIE C L, et al. Adaptive dual-plateaus histogram equalization based on coefficient of variation combined with feedback control principle[J]. Infrared Technology,2019,41(3):262-267.)
[5] 戴声奎, 钟峥, 黄正暐. 基于最大熵模型的双直方图均衡算法[J]. 电子学报,2019,47(3):678-685. (DAI S K,ZHONG Z, HUANG Z W. Maximum entropy model based bi-histogram equalization algorithm[J]. Acta Electronica Sinica,2019,47(3):678-685.)
[6] 胡韦伟, 汪荣贵, 方帅, 等. 基于双边滤波的Retinex图像增强算法[J]. 工程图学学报,2010,31(2):104-109.(HU W W, WANG R G, FANG S, et al. Retinex algorithm for image enhancement based on bilateral filtering[J]. Journal of Engineering Graphics,2010,31(2):104-109.)
[7] HE K,JIAN S,TANG X. Single image haze removal using dark channel prior[C]//Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2009:1956-1963.
[8] DONG X, PANG Y, WEN J. Fast efficient algorithm for enhancement of low lighting video[C]//Proceedings of the ACM SIGGRAPH 2010 Posters. New York:ACM,2010:No. 69.
[9] 毛东月, 谢正祥, 贺向前, 等. 自适应双向保带宽对数变换及低照度图像增强[J]. 中国图象图形学报,2017,22(10):1356-1363. (MAO D Y,XIE Z X,HE X Q,et al. Adaptive bilateral logarithm transformation with bandwidth preserving and low-illumination image enhancement[J]. Journal of Image and Graphics,2017,22(10):1356-1363.)
[10] 王殿伟, 王晶, 许志杰, 等. 一种光照不均匀图像的自适应校正算法[J]. 系统工程与电子技术,2017,39(6):1383-1390. (WANG D W,WANG J,XU Z J,et al. An adaptive correction algorithm for non-illumination images[J]. System Engineering and Electronic Technology,2017,39(6):1383-1390.)
[11] HE K,SUN J,TANG X. Guided image filtering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2013, 35(6):1397-1409.
[12] GONZALEZ R C,WOODS R E. 数字图像处理[M]. 3版. 北京:电子工业出版社,2017:63-66. (GONZALEZ R C,WOODS R E. Digital Image Processing[M]. 3rd ed. Beijing:Publishing House of Electronics Industry,2017:63-66.)
[13] 王晓华, 赵志雄. 结合伽马变换和小波变换的PCA人脸识别算法[J]. 计算机工程与应用,2016,52(5):190-193. (WANG X H,ZHAO Z X. PCA face recognition algorithm combined with Gamma transform and wavelet transform[J]. Computer Engineering and Applications,2016,52(5):190-193.)
[14] 刘志成, 王殿伟, 刘颖, 等. 基于二维伽马函数的光照不均匀图像自适应校正算法[J]. 北京理工大学学报,2016,36(2):191-196,214. (LIU Z C,WANG D W,LIU Y,et al. Adaptive adjustment algorithm for non-uniform illumination image based on 2D Gamma function[J]. Journal of Beijing Institute of Technology,2016,36(2):191-196,214.)

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最大差值图决策的低照度图像自适应增强算法

Adaptive enhancement algorithm of low illumination image based on maximum difference image decision

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