直方图均衡化与CNN对比度增强技术的对比分析

R. Vaddi, L. Boggavarapu, H. D. Vankayalapati, K. R. Anne
{"title":"直方图均衡化与CNN对比度增强技术的对比分析","authors":"R. Vaddi, L. Boggavarapu, H. D. Vankayalapati, K. R. Anne","doi":"10.1109/ICOAC.2011.6165157","DOIUrl":null,"url":null,"abstract":"Contrast enhancement is one of the primary aspects in computer vision. In order to understand the image, the contrast of the image should be clear. In many scenarios, especially in biomedical images, security and surveillance, the visual quality of source images or video is not up to the expected quality. There exist many algorithms such as histogram equalization, genetic algorithms and neural networks to improve the contrast of the images. In this work, we summarized the state of the art and made comparative study among contrast enhancement techniques. Comparisons are done in two cases: one among the histogram based techniques, another between histogram based techniques and method using Cellular Neural Networks (CNN). The method using CNN proved to perform better than the conventional techniques.","PeriodicalId":369712,"journal":{"name":"2011 Third International Conference on Advanced Computing","volume":"66 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Comparative analysis of contrast enhancement techniques between histogram equalization and CNN\",\"authors\":\"R. Vaddi, L. Boggavarapu, H. D. Vankayalapati, K. R. Anne\",\"doi\":\"10.1109/ICOAC.2011.6165157\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Contrast enhancement is one of the primary aspects in computer vision. In order to understand the image, the contrast of the image should be clear. In many scenarios, especially in biomedical images, security and surveillance, the visual quality of source images or video is not up to the expected quality. There exist many algorithms such as histogram equalization, genetic algorithms and neural networks to improve the contrast of the images. In this work, we summarized the state of the art and made comparative study among contrast enhancement techniques. Comparisons are done in two cases: one among the histogram based techniques, another between histogram based techniques and method using Cellular Neural Networks (CNN). The method using CNN proved to perform better than the conventional techniques.\",\"PeriodicalId\":369712,\"journal\":{\"name\":\"2011 Third International Conference on Advanced Computing\",\"volume\":\"66 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 Third International Conference on Advanced Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICOAC.2011.6165157\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 Third International Conference on Advanced Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICOAC.2011.6165157","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

摘要

对比度增强是计算机视觉的主要研究方向之一。为了理解图像,图像的对比度要清晰。在许多场景中,特别是在生物医学图像、安防和监控中,源图像或视频的视觉质量达不到预期的质量。提高图像对比度的算法有直方图均衡化、遗传算法和神经网络等。在本文中,我们总结了对比度增强技术的现状,并对各种对比度增强技术进行了比较研究。在两种情况下进行比较:一种是基于直方图的技术,另一种是基于直方图的技术和使用细胞神经网络(CNN)的方法之间的比较。事实证明,使用CNN的方法比传统技术性能更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Comparative analysis of contrast enhancement techniques between histogram equalization and CNN
Contrast enhancement is one of the primary aspects in computer vision. In order to understand the image, the contrast of the image should be clear. In many scenarios, especially in biomedical images, security and surveillance, the visual quality of source images or video is not up to the expected quality. There exist many algorithms such as histogram equalization, genetic algorithms and neural networks to improve the contrast of the images. In this work, we summarized the state of the art and made comparative study among contrast enhancement techniques. Comparisons are done in two cases: one among the histogram based techniques, another between histogram based techniques and method using Cellular Neural Networks (CNN). The method using CNN proved to perform better than the conventional techniques.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
相关文献
二甲双胍通过HDAC6和FoxO3a转录调控肌肉生长抑制素诱导肌肉萎缩
IF 8.9 1区 医学Journal of Cachexia, Sarcopenia and MusclePub Date : 2021-11-02 DOI: 10.1002/jcsm.12833
Min Ju Kang, Ji Wook Moon, Jung Ok Lee, Ji Hae Kim, Eun Jeong Jung, Su Jin Kim, Joo Yeon Oh, Sang Woo Wu, Pu Reum Lee, Sun Hwa Park, Hyeon Soo Kim
具有疾病敏感单倍型的非亲属供体脐带血移植后的1型糖尿病
IF 3.2 3区 医学Journal of Diabetes InvestigationPub Date : 2022-11-02 DOI: 10.1111/jdi.13939
Kensuke Matsumoto, Taisuke Matsuyama, Ritsu Sumiyoshi, Matsuo Takuji, Tadashi Yamamoto, Ryosuke Shirasaki, Haruko Tashiro
封面:蛋白质组学分析确定IRSp53和fastin是PRV输出和直接细胞-细胞传播的关键
IF 3.4 4区 生物学ProteomicsPub Date : 2019-12-02 DOI: 10.1002/pmic.201970201
Fei-Long Yu, Huan Miao, Jinjin Xia, Fan Jia, Huadong Wang, Fuqiang Xu, Lin Guo
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Keynote speaker I: Ubiquitous sensing Bio-molecular event extraction using Support Vector Machine Genetically optimized ANFIS based Intelligent Navigation System An efficient clusterhead election algorithm based on maximum weight for MANET A novel business model for enterprise service logic change management
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1