Haze removal for aerial degraded images

JI Xiao-qiang; DAI Ming; YIN Chuan-li; FENG Yu-ping; BAI Xu-guang

doi:10.3788/OPE.20111907.1659

您当前的位置：

首页 >

文章列表页 >

Haze removal for aerial degraded images

Article | 更新时间：2020-08-12

- Haze removal for aerial degraded images
- Optics and Precision Engineering Vol. 19, Issue 7, Pages: 1659-1668(2011)
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所,吉林长春,中国,130033
  2. 中国科学院研究生院北京,100039
  3. 青岛科技大学,山东青岛,266042
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20111907.1659
  CLC： TP391.4;TP751.1
- Received：12 October 2010，
  
  Revised：17 November 2010，
  
  Published Online：25 July 2011，
  
  Published：25 July 2011
- 稿件说明：
移动端阅览
嵇晓强, 戴明, 尹传历, 冯宇平, 柏旭光. 航拍降质图像的去雾处理[J]. 光学精密工程, 2011,19(7): 1659-1668

JI Xiao-qiang, DAI Ming, YIN Chuan-li, FENG Yu-ping, BAI Xu-guang. Haze removal for aerial degraded images[J]. Editorial Office of Optics and Precision Engineering, 2011,19(7): 1659-1668
嵇晓强, 戴明, 尹传历, 冯宇平, 柏旭光. 航拍降质图像的去雾处理[J]. 光学精密工程, 2011,19(7): 1659-1668 DOI： 10.3788/OPE.20111907.1659.

JI Xiao-qiang, DAI Ming, YIN Chuan-li, FENG Yu-ping, BAI Xu-guang. Haze removal for aerial degraded images[J]. Editorial Office of Optics and Precision Engineering, 2011,19(7): 1659-1668 DOI： 10.3788/OPE.20111907.1659.

摘要

针对有雾天气下无人机航拍视觉系统的能见度低

航拍图像对比度和色彩保真度差等问题

基于暗原色先验规律以及雾图的物理模型提出了一种雾天降质图像去雾处理技术。从图像复原和增强两个角度出发

分别建立了户外图像全局去雾和对比度自适应调整的最优化模型

从而能够直接复原得到高质量的去除雾干扰的图像并且估算出雾的浓度。对一系列户外带雾图像的分组实验表明

该方法可以快速有效地提高带雾图像的对比度和色彩清晰度

获得满意的视觉效果。另外

该方法克服了 Kaiming He方法处理时间过长的缺陷

平均处理时间仅为原方法的10%左右

显著缩短了运算时间

为在工程项目中实现图像的实时去雾处理提供了理论依据。

Abstract

For aerial images with poor contrast and color fidelity due to foggy and hazy weathers

this paper proposes a technique of haze removal for aerial degraded images based on the dark-channel prior and the physical model to improve the visibility of vision system in an Unmanned Aerial Vehicle. From the viewpoints of image restoration and image enhancement

the optimized models of global haze removal and self-adapting contract extending are established

respectively. Using the method

a high quality haze-free image can be recovered and the thickness of the haze can be also established. The experimental results on a variety of outdoor haze images demonstrate that it can enhance the contrast and color definition of hazy degraded images fast and efficiently and can achieve satisfactory visual effects. Moreover

the method overcomes the Kaiming Hes drawback of more time consuming

and the aver- age processing time is 10% that of the traditional method.It provides a theoretical reference for the real-time haze removal processing in engineering projects.

关键词

Keywords

references

刘瑞剑.低能见度条件下图像清晰化处理研究. 太原:中北大学,2008. LIU R J. Research on image clear processing under low visibility condition.Taiyuan: North University of China, 2008.(in Chinese)[2] KOPF J,NEUBERT B,CHEN B, et al. Deep photo: Model-based photograph enhancement and viewing [J]. ACM Transactions on Graphics, SIGGRAPH, California, USA, 2008, 5:1-10.[3] NARASIMHAN S G,NAYAR S K. Chromatic framework for vision in bad weather. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, New York, USA, 2000, 1:598-605. [4] NARASIMHAN S G,NAYAR S K. Contrast restoration of weather degraded images [J]. Proceedings of IEEE Transactions on Pattern Analysis and Machine Intelligence, Berlin, Germany, 2003, 7:713-724.[5] SHWARTZ S,NAMER E,SCHECHNER Y Y. Blind haze sep-araion. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Beijing China, 1:1984-1991.[6] TAN R. Visibility in bad weather from a single image. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Alaska, USA, 2008:1-8.[7] FATAL R. Single image dehazing[J].ACM Transactions on Graphics, SIGGRAPH , 2008,27(3):1-9.[8] TAREL J P.Fast visibility restoration from a single color or gray level image. Proceedings of IEEE Conference on International Conference on Computer Vision, Kyoto, Japan, 2009:20-28.[9] HE K, SUN J, TANG X O. Single image haze removal using dark channel prior. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Miami, 2009:1956-1963.[10] 杨靖宇,张永生,邹晓亮,等. 利用暗原色先验知识实现航空影像快速去雾[J]. 武汉大学学报,2010,11:1292-1295. YANG J Y, ZHANG Y SH, ZOU X L, et al. Using dark channel prior to quickly remove haze from a single image [J].Geomantic and Information Science o f Wuhan University, 2010,11:1292-1295.[11] 赵莹.基于单幅图像的去雾方法研究. 天津:天津大学,2009. ZHAO Y.Research on single image-based Dehazing Algorithm. Tianjin: Tianjin University, 2009.(in Chinese)[12] CHAVEZ P. An improved dark-object subtraction technique for atmospheric scatting correction of multispectral data [J]. Remote Sensing of Environment, 1988, 24:450-479.[13] PREETHAM A J,SHIRLEY P, SMITS B. A practical analytic model for daylight [J]. Special Interest Group for Computer GRAPHICS, SIGGRAPH, 1999,4:91-100.[14] NARASIMHAN S G, NAYAR S K. Interactive (de) weathering of an image using physical model. Proceedings of IEEE Conference on International Conference on Computer Vision, Nice, France, 2003:65-71.[15] 吴家伟,武春风,庹文波. 红外图像实时显示增强系统设计[J]. 光学精密工程,2009,17(10):2612-2619. WU J W,WU CH F,TUO W B. Design of real-time infrared image enhancing system[J]. Opt. Precision Eng., 2009,17(10):2612-2619. (in Chinese)[16] 葛微,李桂菊,程宇奇,等. 利用改进的Retinex进行人脸图像光照处理[J]. 光学精密工程,2010,18(4):1011-1020. GE W, LIG J, CHENG Y Q. Face image illumination processing based on improved Retinex[J]. Opt. Precision Eng., 2010,18(4):1011-1020. (in Chinese)[17] JI X Q, FENG Y P, DAI M, et al. Real-time defogging processing of aerial images. Proceedings of IEEE International Conference on Wireless Communications Networking and Mobile Computing, Chengdu, China , 2010:1556-1600.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Adaptive color compensation and multi-scale fusion for underwater image enhancement

Degradation remote sensing image quality enhancement based on frequency-domain-spatial-domain hybrid attention

Underwater image enhancement using joint texture perception and color histogram features

Cross-level feature aggregation image enhancement with dual-path hybrid attention

Low-light image enhancement based on the fusion of Bilateral filter MSR and AutoMSRCR

Related Author

LIU Cuiyin

JING Tengyun

WANG Feng

CHEN Yuanshuai

WEI Hua

TANG Xiongxin

NIE Haitao

WANG Jing

Related Institution

School of Information Engineering and Automation， Kunming University of Science and Technology

School of Physics and Material Sciences， Guangzhou University

Laboratory of Science and Technology on Integrated Information System， Institute of Software， Chinese Academy of Sciences

Changchun Institute of Optics，Fine Mechanics and Physics，Chinese Academy of Sciences

Department of emergency management， Institute of Disaster Prevention

AI问答

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China Postal code：130033
Tel：0431-86176855 Email：gxjmgc@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰