Single Image Dehazing with V-transform and Dark Channel Prior

Xiaochun Wang, Xiangdong Sun, Ruixia Song
{"title":"Single Image Dehazing with V-transform and Dark Channel Prior","authors":"Xiaochun Wang, Xiangdong Sun, Ruixia Song","doi":"10.21078/JSSI-2020-185-10","DOIUrl":null,"url":null,"abstract":"Abstract Single image dehazing algorithm based on the dark channel prior may cause block effect and color distortion. To improve these limitations, this paper proposes a single image dehazing algorithm based on the V-transform and the dark channel prior, in which a hazy RGB image is converted into the HSI color space, and each component H, I and S is processed separately. The hue component H remains unchanged, the saturation component S is stretched after being denoised by a median filter. In the procession of intensity component, a quad-tree algorithm is presented to estimate the atmospheric light, the dark channel prior and the V-transform are used to estimate the transmission map. To reduce the computational complexity, the intensity component I is decomposed by the V-transform first, coarse transmission map is then estimated by applying the dark channel prior on the low frequency reconstruction image, and the guided filter is finally employed to refine the coarse transmission map. For images with sky regions, the haze removal effectiveness can be greatly improved by just increasing the minimum value of the transmission map. The proposed algorithm has low time complexity and performs well on a wide variety of images. The recovered images have more nature color and less color distortion compared with some state-of-the-art methods.","PeriodicalId":258223,"journal":{"name":"Journal of Systems Science and Information","volume":"195 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Systems Science and Information","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21078/JSSI-2020-185-10","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Abstract Single image dehazing algorithm based on the dark channel prior may cause block effect and color distortion. To improve these limitations, this paper proposes a single image dehazing algorithm based on the V-transform and the dark channel prior, in which a hazy RGB image is converted into the HSI color space, and each component H, I and S is processed separately. The hue component H remains unchanged, the saturation component S is stretched after being denoised by a median filter. In the procession of intensity component, a quad-tree algorithm is presented to estimate the atmospheric light, the dark channel prior and the V-transform are used to estimate the transmission map. To reduce the computational complexity, the intensity component I is decomposed by the V-transform first, coarse transmission map is then estimated by applying the dark channel prior on the low frequency reconstruction image, and the guided filter is finally employed to refine the coarse transmission map. For images with sky regions, the haze removal effectiveness can be greatly improved by just increasing the minimum value of the transmission map. The proposed algorithm has low time complexity and performs well on a wide variety of images. The recovered images have more nature color and less color distortion compared with some state-of-the-art methods.
单幅图像去雾与v变换和暗通道先验
摘要基于暗通道先验的单幅图像去雾算法存在块效应和色彩失真的问题。为了改善这些局限性,本文提出了一种基于v变换和暗通道先验的单幅图像去雾算法,将模糊的RGB图像转换为HSI色彩空间,分别对H、I、S分量进行处理。色相分量H保持不变,饱和度分量S经过中值滤波去噪后拉伸。在光强分量处理中,采用四叉树算法估计大气光,利用暗信道先验和v变换估计传输图。为了降低计算复杂度,首先对强度分量I进行v变换分解,然后对低频重建图像应用暗信道先验估计粗透射图,最后利用引导滤波器对粗透射图进行细化。对于有天空区域的图像,只需增加透射图的最小值,就可以大大提高去霾效果。该算法具有时间复杂度低、适用范围广的特点。与现有的方法相比,恢复的图像具有更多的自然色彩和更小的色彩失真。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信