利用上下边界估计的图像增强方法

doi:10.3969/j.issn.1001-2400.2015.01.024

J4 ›› 2015, Vol. 42 ›› Issue (1): 149-154.doi: 10.3969/j.issn.1001-2400.2015.01.024

利用上下边界估计的图像增强方法

张明;白松浩

(中国人民解放军95899部队，北京 100085)

收稿日期:2013-10-17 出版日期:2015-02-20 发布日期:2015-04-14
通讯作者: 张明
作者简介:张明(1981-)，男，博士，E-mail: zmshy2128@163.com．

Image enhancement based on estimating the upper and lower boundary

ZHANG Ming;BAI Songhao

(The Unit 95899 of the PLA, Beijing 100085, China)

Received:2013-10-17 Online:2015-02-20 Published:2015-04-14
Contact: ZHANG Ming

摘要/Abstract

摘要：

提出了一种基于上下边界估计的快速图像增强方法．该方法使用上边界提升图像的细节信息，使用下边界消除图像的雾霾成分，调整上下边界的距离可灵活控制图像增强的程度，在上下边界计算过程中采用了加权中值滤波，能够在有效去噪的同时保持图像边界清晰．实验结果表明，该算法能有效改善图像质量，保持图像细节信息，压缩图像动态范围，色彩逼近于真实环境，且避免了光晕的产生．

关键词: 图像增强, 上下边界, Retinex理论

Abstract:

A novel image enhancement algorithm based on estimating the upper and lower boundary is presented. The method employs the upper and lower boundary to adjust the image details adaptively and dehaze the image, respectively and control the extent of image enhancement flexibly by adjusting the distance between the upper and lower boundary. The weighted median filter is adopted to calculate the upper and lower boundary for preserving image edges. Simulation results show that the proposed algorithm can improve the visual quality of the image efficiently, preserve the details, compress the high dynamic range, reduce halo artifacts and make the image more realistic and natural.

Key words: image enhancement, estimating the upper and lower boundary, Retinex theory

中图分类号:

TN919.81

张明;白松浩. 利用上下边界估计的图像增强方法[J]. J4, 2015, 42(1): 149-154.

ZHANG Ming;BAI Songhao. Image enhancement based on estimating the upper and lower boundary[J]. J4, 2015, 42(1): 149-154.

参考文献

［1］ Funt B, Ciurea F, McCann J. Retinex in Matlab［J］. Journal of Electronic Imaging, 2004,13(1): 48-57.
［2］ Jobson D J, Rahman Z U, Woodell G A. Properties and Performance of a Center/Surround Retinex［J］. IEEE Transactions on Image Processing, 1997, 6(3): 451-462.
［3］马时平, 张明, 毕笃彦, 等. 一种细节保持的Retinex图像增强算法［J］. 西安电子科技大学学报, 2010, 37(3): 541-546.
Ma Shiping, Zhang Ming, Bi Duyan, et al. Novel Detail Preserving Retinex Algorithm for Image Enhancement［J］. Journal of Xidian University, 2010, 37(3): 541-546.
［4］ Banic N, Loncaric S. Light Random Sprays Retinex: Exploiting the Noisy Illumination Estimation［J］. IEEE Signal Processing Letters, 2013, 20(12): 1240-1243.
［5］ He Kaiming, Sun Jian, Tang Xiaoou. Single Image Haze Removal Using Dark Channel Prior［C］//Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2009: 1956-1963.
［6］ Tarel J P, Hautibre N. Fast Visibility Restoration from a Single Color or Gray Level Image［C］//Proceedings of the 12th IEEE Intemational Conference on Computer Vision. Piscataway: IEEE, 2009: 2201-2208.
［7］ Tarel J P, Hautibre N, Caraffa L, et al. Vision Enhancement in Homogeneous and Heterogeneous Fog［J］. IEEE Intelligent Transportation Systems Magazine, 2012, 4(2): 6-20.
［8］ Cheng F C, Lin C H, Lin J L. Constant Time O(1) Image Fog Removal Using Lowest Level Channel［J］. Electronics Letters, 2012, 48(22): 1404-1406.
［9］汪荣贵, 傅剑峰, 杨志学, 等. 基于暗原色先验模型的Retinex算法［J］. 电子学报, 2013, 41(6): 1188-1192.
Wang Ronggui, Fu Jianfeng, Yang Zhixue, et al. A Novel Retinex Algorithm Based on Dark Channel Prior Model［J］. Acta Electronica Sinica, 2013, 41(6): 1188-1192.
［10］ He Kaiming, Sun Jian, Tang Xiaoou. Guided Image Filtering［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6): 1397-1409.
［11］ Meylan L, Sosstrunk S. High Dynamic Range Image Rendering with a Retinex-based Adaptive Filter［J］. IEEE Transactions on Image Processing, 2006,15(9): 2820-2830.
［12］ Kim K, Bae J, Jaeseok K. Natural HDR Image Tone Mapping Based on Retinex［J］. IEEE Transactions on Consumer Electronics, 2011, 57(4): 1807-1814.

[1]	孙彦景,魏力,张年龙,云霄,董锴文,葛敏,程小舟,侯晓峰. 联合DD-GAN和全局特征的井下人员重识别方法[J]. 西安电子科技大学学报, 2021, 48(5): 201-211.
[2]	王俊祥,黄霖,张影,倪江群,林朗. 低复杂度的增强图像来源检测算法[J]. 西安电子科技大学学报, 2021, 48(1): 96-106.
[3]	刘志强,张铭津,池源,李云松. 一种深度学习的硬件木马检测算法[J]. 西安电子科技大学学报, 2019, 46(6): 37-45.
[4]	冯玉颖;王俊平;马塾亮;李超;伍尧. COPLIP新模型及其图像增强新算法[J]. 西安电子科技大学学报, 2018, 45(1): 66-71.
[5]	崔华;周婷洁;郭璐;袁超;宋焕生. 改进的偏微分扩散方程图像增强方法[J]. J4, 2014, 41(6): 195-198.
[6]	李锦;王俊平;万国挺;李紫阳;许丹;曹洪花;张广燕. 一种结合直方图均衡化和MSRCR的图像增强新算法[J]. J4, 2014, 41(3): 103-109.
[7]	张广燕;王俊平;汪松;曹洪花;许丹;李锦;李长江. 一种SLIP模型的图像增强新算法[J]. J4, 2014, 41(1): 105-109+169.
[8]	李权合;查宇飞;熊磊;毕笃彦. 雾霾退化图像场景再现新算法[J]. J4, 2013, 40(5): 99-106.
[9]	李权合;毕笃彦;何林远. 退化过程模拟模型及其在图像增强中的应用[J]. J4, 2011, 38(6): 185-192.
[10]	马时平;张明;毕笃彦;许悦雷. 一种细节保持的Retinex图像增强算法[J]. J4, 2010, 37(3): 541-546.
[11]	尹奎英;胡利平;刘宏伟;金林. 一种复合的SAR图像去噪算法[J]. J4, 2010, 37(2): 224-230.
[12]	马苗;焦莉莉. 结构相似性灰关联在强噪声图像增强中的应用[J]. J4, 2010, 37(2): 346-352+365.
[13]	寇小明;刘上乾;洪鸣;汪大宝. 一种自适应红外图像增强技术[J]. J4, 2009, 36(6): 1070-1074.
[14]	简耀波1;2;阮双琛2;秦翰林3;周慧鑫3. 基于提升格式小波变换和Snake模型的红外目标边缘增强与分割算法 [J]. J4, 2007, 34(7): 136-139.
[15]	杨占龙;郭宝龙. 一种新的碎片图像镶嵌技术 [J]. J4, 2007, 34(2): 249-253.

利用上下边界估计的图像增强方法

Image enhancement based on estimating the upper and lower boundary

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 0