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A self-adaptive Contrast Enhancement Method Based on Gradient and Intensity Histogram for Remote Sensing Images
Chen, Tieqiao1,2; Liu, Jiahang1; Su, Xiuqin(苏秀琴)1; Liu, Jia1; Zhu, Feng1,2; Wang, Yihao1; Chen, TQ (reprint author), Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China.
2017
会议名称2nd International Conference on Computer Engineering, Information Science and Application Technology (ICCIA)
会议录名称PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON COMPUTER ENGINEERING, INFORMATION SCIENCE & APPLICATION TECHNOLOGY (ICCIA 2017)
卷号74
页码324-328
会议日期2017-07-08
会议地点Wuhan, PEOPLES R CHINA
出版地PARIS
出版者ATLANTIS PRESS
产权排序1
摘要

This paper proposes an efficient method to modify gradient and intensity histograms (GIH) for contrast enhancement, which plays an important role in remote sensing image processing and information extraction. First, a self-adaptive algorithm is used to flatten the shape of GIH of input image according to standard deviation of GIH. Then, the standard lookup table-based histogram equalization procedure is applied to get well enhanced image. Experimental results, using various remote sensing images, show that the proposed method generates enhanced images with more information and higher visual quality, compared with several conventional methods.

关键词Contrast Enhancement Remote Sensing Image Self-adaptive Algorithm Gradient And Intensity Histogram
学科领域Computer Science, Theory & Methods
作者部门遥感与智能信息系统研究中心
收录类别ISTP
ISBN号978-94-6252-361-6
语种英语
文献类型会议论文
条目标识符http://ir.opt.ac.cn/handle/181661/30003
专题遥感与智能信息系统研究中心
通讯作者Chen, TQ (reprint author), Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China.
作者单位1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714
Chen, Tieqiao,Liu, Jiahang,Su, Xiuqin,et al. A self-adaptive Contrast Enhancement Method Based on Gradient and Intensity Histogram for Remote Sensing Images[C]. PARIS:ATLANTIS PRESS,2017:324-328.
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