[1]牟琦,魏妍妍,李姣,等.改进的Retinex低照度图像增强算法研究[J].哈尔滨工程大学学报,2018,39(12):2001-2010.[doi:10.11990/jheu.201711096]
 MU Qi,WEI Yanyan,LI Jiao,et al.Research on the improved Retinex algorithm for low-illumination image enhancement[J].hebgcdxxb,2018,39(12):2001-2010.[doi:10.11990/jheu.201711096]
点击复制

改进的Retinex低照度图像增强算法研究(/HTML)
分享到:

《哈尔滨工程大学学报》[ISSN:1006-6977/CN:61-1281/TN]

卷:
39
期数:
2018年12期
页码:
2001-2010
栏目:
出版日期:
2018-12-05

文章信息/Info

Title:
Research on the improved Retinex algorithm for low-illumination image enhancement
作者:
牟琦12 魏妍妍1 李姣1 李洪安1 李占利1
1. 西安科技大学 计算机科学与技术学院, 陕西 西安 710054;
2. 西安科技大学 机械工程学院, 陕西 西安 710054
Author(s):
MU Qi12 WEI Yanyan1 LI Jiao1 LI Hongan1 LI Zhanli1
1. College of Computer Science and Technology, Xi’an University of Science and Technology, Xi’an 710054, China;
2. School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
关键词:
低照度图像Retinex图像增强引导滤波低秩分解稀疏噪声
分类号:
TP391.41
DOI:
10.11990/jheu.201711096
文献标志码:
A
摘要:
针对Retinex算法处理低照度图像时会出现细节丢失、边缘模糊等现象,本文采用引导滤波和低秩分解对Retinex算法进行了改进。该算法在采用多尺度Retinex提升图像亮度、得到反射分量后,采用引导滤波和高频提升对图像的反射分量进行细节增强;然后,运用全局低秩分解算法去除稀疏噪声,有效地消除了低照度图像中的噪声,以及高频提升过程中产生的噪声。实验表明:该算法不仅能够有效的提高图像的亮度和对比度,同时也保留了原始图像中丰富的边缘和细节信息,并有效去除了图像噪声,图像的视觉效果与客观评价结果也都取得了较大提升。将该算法应用于低照度环境下的人脸检测,检测率也得到了提高。

参考文献/References:

[1] LAND E H. The retinex[J]. American scientist, 1964, 52(2):247-253, 255-264.
[2] GONZALEZ R C, WOODS R E. Digital image processing Addison-Wesley publishing company[C]//Digital Restoration of Film and Video Archives (Ref. No. 2001/049). IEE Seminar on. IET, 1993:6/1-6/5.
[3] LAND E H, MCCANN J J. Lightness and Retinex theory[J]. Journal of the optical society of America, 1971, 61(1):1-11.
[4] LAND E H. Recent advances in retinex theory and some implications for cortical computations:color vision and the natural image[J]. Proceedings of the national academy of sciences of the United States of America, 1983, 80(16):5163-5169.
[5] FRANKLE J A, MCCANN J J. Method and apparatus for lightness imaging:USA, 4384336[P]. 1983-05-17.
[6] LAND E H. An alternative technique for the computation of the designator in the Retinex theory of color vision[J]. Proceedings of the national academy of sciences of the United States of America, 1986, 83(10):3078-3080.
[7] 余春艳, 徐小丹, 林晖翔, 等. 应用雾天退化模型的低照度图像增强[J]. 中国图象图形学报, 2017, 22(9):1194-1205.YU Chunyan, XU Xiaodan, LIN Huixiang, et al. Low-illumination image enhancement method based on a fog-degraded model[J]. Journal of image and graphics, 2017, 22(9):1194-1205.
[8] 黄丽雯, 杨欢欢, 王勃. 多方法结合人脸图像光照补偿算法研究及改进[J]. 重庆理工大学学报(自然科学), 2017, 31(11):179-184.HUANG Liwen, YANG Huanhuan, WANG Bo. Research and improvement of multi-methods combining face image illumination compensation algorithm[J]. Journal of Chongqing university of technology (natural science), 2017, 31(11):179-184.
[9] TOMASI C, MANDUCHI R. Bilateral filtering for gray and color images[C]//Proceedings of Sixth International Conference on Computer Vision. Bombay, India, 1998:839-846.
[10] 刘晓阳, 乔通, 乔智. 基于双边滤波和Retinex算法的矿井图像增强方法[J]. 工矿自动化, 2017, 43(2):49-54.LIU Xiaoyang, QIAO Tong, QIAO Zhi. Image enhancement method of mine based on bilateral filtering and Retinex algorithm[J]. Industry and mine automation, 2017, 43(2):49-54.
[11] 刘海波, 汤群芳, 杨杰. 改进直方图均衡和Retinex算法在灰度图像增强中的应用[J]. 量子电子学报, 2014, 31(5):525-532.LIU Haibo, TANG Qunfang, YANG Jie. Application of improved histogram equalization and Retinex algorithm in gray image enhancement[J]. Chinese journal of quantum electronics, 2014, 31(5):525-532.
[12] HE Kaiming, SUN Jian, TANG Xiaoou. Guided image filtering[C]//Proceedings of the 11th European Conference on Computer Vision. Berlin, Heidelberg, 2010:1-14.
[13] KIMMEL R, ELAD M, SHAKED D, et al. A variational framework for Retinex[J]. International journal of computer vision, 2003, 52(1):7-23.
[14] TAO Li, ASARI V. Modified luminance based MSR for fast and efficient image enhancement[C]//Applied Imagery Pattern Recognition Workshop, 2003. Proceedings. Washington, USA, 2003:174-179.
[15] CANDÈS E J, LI Xiaodong, MA Yi, et al. Robust principal component analysis?[J]. Journal of the ACM, 2011, 58(3):11.
[16] WRIGHT J, PENG Yigang, MA Yi, et al. Robust principal component analysis:exact recovery of corrupted low-rank matrices by convex optimization[C]//Neural Networks for Signal Processing X, 2000. Proceedings of the 2000 IEEE Signal Processing Society Workshop. 2002:289-298.
[17] LIN Zhouchen, GANESH A, WRIGHT J, et al. Fast convex optimization algorithms for exact recovery of a corrupted low-rank matrix[J]. Journal of the marine biological association of the UK, 2009, 56(3):707-722.
[18] JOBSON D J, RAHMAN Z U, WOODELL G A. Statistics of visual representation[C]//Proceeding of Volume 4736, Visual Information Processing XI. Orlando, 2002, 4736:25-35.
[19] 冯策, 戴树岭. 遥现中基于显著特征的深度图像滤波算法[J]. 哈尔滨工程大学学报, 2014, 35(11):1364-1368.FENG Ce, DAI Shuling. The depth map filter algorithm based on salient features in telepresence[J]. Journal of Harbin Engineering University, 2014, 35(11):1364-1368.
[20] 杨万挺, 汪荣贵, 方帅, 等. 滤波器可变的Retinex雾天图像增强算法[J]. 计算机辅助设计与图形学学报, 2010, 22(6):965-971.YANG Wanting, WANG Ronggui, FANG Shuai, et al. Variable filter Retinex algorithm for foggy image enhancement[J]. Journal of computer-aided design & computer graphics, 2010, 22(6):965-971.

备注/Memo

备注/Memo:
收稿日期:2017-11-26。
基金项目:陕西省教育厅科研计划项目(16JK1497).
作者简介:牟琦(1974-),女,副教授.
通讯作者:牟琦,E-mail:mu_qi@xust.edu.cn.
更新日期/Last Update: 2018-12-01