单幅图像去雾方法的不同雾霾图像条件

Noor Asma Husain, M. Rahim, Huma Chaudhry
{"title":"单幅图像去雾方法的不同雾霾图像条件","authors":"Noor Asma Husain, M. Rahim, Huma Chaudhry","doi":"10.1109/SSCI50451.2021.9659889","DOIUrl":null,"url":null,"abstract":"The dust, mist, haze, and smokiness of the atmosphere typically degrade images from the light and absorption. These effects have poor visibility, dimmed luminosity, low contrast, and distortion of colour. As a result, restoring a degraded image is difficult, especially in hazy conditions. The image dehazing method focuses on improving the visibility of image details while preserving image colours without causing data loss. Many image dehazing methods achieve the goal of removing haze while also addressing other issues such as oversaturation, colour distortion, and halo artefacts. However, the limitation of haze level rendered these approaches ineffective. A volume of various haze level data is required to demonstrate the efficiency of the image dehazing method in removing haze at all haze levels and obtaining the image's quality. This paper introduced a dynamic scattering coefficient to the dehazing algorithm for determining an applicable visibility range for different haze conditions. These proposed methods improve on the current state-of-the-art dehazing method in terms of image quality measurement results.","PeriodicalId":255763,"journal":{"name":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Different Haze Image Conditions for Single Image Dehazing Method\",\"authors\":\"Noor Asma Husain, M. Rahim, Huma Chaudhry\",\"doi\":\"10.1109/SSCI50451.2021.9659889\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The dust, mist, haze, and smokiness of the atmosphere typically degrade images from the light and absorption. These effects have poor visibility, dimmed luminosity, low contrast, and distortion of colour. As a result, restoring a degraded image is difficult, especially in hazy conditions. The image dehazing method focuses on improving the visibility of image details while preserving image colours without causing data loss. Many image dehazing methods achieve the goal of removing haze while also addressing other issues such as oversaturation, colour distortion, and halo artefacts. However, the limitation of haze level rendered these approaches ineffective. A volume of various haze level data is required to demonstrate the efficiency of the image dehazing method in removing haze at all haze levels and obtaining the image's quality. This paper introduced a dynamic scattering coefficient to the dehazing algorithm for determining an applicable visibility range for different haze conditions. These proposed methods improve on the current state-of-the-art dehazing method in terms of image quality measurement results.\",\"PeriodicalId\":255763,\"journal\":{\"name\":\"2021 IEEE Symposium Series on Computational Intelligence (SSCI)\",\"volume\":\"52 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-12-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE Symposium Series on Computational Intelligence (SSCI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SSCI50451.2021.9659889\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SSCI50451.2021.9659889","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

大气中的灰尘、薄雾、薄雾和烟雾通常会降低光和吸收的图像。这些效果具有可视性差、亮度变暗、对比度低和色彩失真的特点。因此,恢复退化的图像是困难的,特别是在朦胧的条件下。该图像去雾方法侧重于在不造成数据丢失的情况下,在保持图像颜色的同时提高图像细节的可见性。许多图像去雾方法实现了去除雾霾的目标,同时也解决了其他问题,如过饱和度,色彩失真和光晕伪影。然而,由于雾霾程度的限制,这些方法都是无效的。需要大量的各种雾霾级别的数据来证明图像去雾方法在去除所有雾霾级别的雾霾和获得图像质量方面的效率。本文将动态散射系数引入到消雾算法中,以确定不同雾霾条件下的能见度范围。这些提出的方法在图像质量测量结果方面改进了当前最先进的除雾方法。
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
Different Haze Image Conditions for Single Image Dehazing Method
The dust, mist, haze, and smokiness of the atmosphere typically degrade images from the light and absorption. These effects have poor visibility, dimmed luminosity, low contrast, and distortion of colour. As a result, restoring a degraded image is difficult, especially in hazy conditions. The image dehazing method focuses on improving the visibility of image details while preserving image colours without causing data loss. Many image dehazing methods achieve the goal of removing haze while also addressing other issues such as oversaturation, colour distortion, and halo artefacts. However, the limitation of haze level rendered these approaches ineffective. A volume of various haze level data is required to demonstrate the efficiency of the image dehazing method in removing haze at all haze levels and obtaining the image's quality. This paper introduced a dynamic scattering coefficient to the dehazing algorithm for determining an applicable visibility range for different haze conditions. These proposed methods improve on the current state-of-the-art dehazing method in terms of image quality measurement results.
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
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Voice Dialog System for Simulated Patient Robot and Detection of Interviewer Nodding Deep Learning Approaches to Remaining Useful Life Prediction: A Survey Evaluation of Graph Convolutions for Spatio-Temporal Predictions of EV-Charge Availability Balanced K-means using Quantum annealing A Study of Transfer Learning in a Generation Constructive Hyper-Heuristic for One Dimensional Bin Packing
×
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