- About Journal
- Articles Online
- For Authors
- For Referees
Dehazing algorithm of monochromatic remote sensing image based on matrix restoration and dark channel theory
- Chinese Journal of Liquid Crystals and Displays Vol. 38, Issue 2, Pages: 225-235(2023)
- 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 130033
- 作者简介：
- 基金信息：National Natural Science Foundation of China(62005266;52105536)
DOI：10.37188/CJLCD.2022-0192
CLC：
扫 描 看 全 文
LIU Ji-wei, WANG Xiao-dong, LI Yun-hui. Dehazing algorithm of monochromatic remote sensing image based on matrix restoration and dark channel theory. [J]. Chinese Journal of Liquid Crystals and Displays 38(2):225-235(2023)
LIU Ji-wei, WANG Xiao-dong, LI Yun-hui. Dehazing algorithm of monochromatic remote sensing image based on matrix restoration and dark channel theory. [J]. Chinese Journal of Liquid Crystals and Displays 38(2):225-235(2023) DOI： 10.37188/CJLCD.2022-0192.
摘要
遥感图像在成像过程中，容易受到云层和雾霾天气的影响，形成带雾图像；同时在下传时，会受到多种因素影响（如发送接收误码、电离层和对流层的随机变化对信号形成扰动等），使图像信息丢失或掺杂噪声。本文针对信息丢失的带雾单色遥感图像，提出了基于矩阵复原和暗通道理论的单色遥感图像去雾算法，通过基于交替方向乘子法（Alternating Direction Method of Multipliers，ADMM）的矩阵复原算法与传统暗通道理论相结合，有效实现了信息丢失下的遥感雾图复原。通过主观评价和客观评价相结合的方式，将本文算法与经典算法对比。结果表明，本文算法得到的结果在直观视觉上效果更好，且相对于信息丢失30%的雾图，6个场景的平均信息熵提升1.665 2，平均峰值信噪比提升11.702 9，平均结构相似性提升0.814 6，客观评价指标结果优异。进一步在不同比例信息丢失情况下进行实验，结果表明，即使在信息大量丢失的情况下，依然能够得到清晰的复原去雾图像。
Abstract
In the imaging process， the remote sensing images are easily affected by clouds and haze weather， and forms foggy images. At the same time， when the data are downloaded， they are affected by various factors （such as transmission and reception errors， signals disturbed by random changes in the ionosphere and troposphere， ,etc,.） making the image information lost or doped with noise. This paper proposes a dehazing algorithm for monochrome remote sensing images based on matrix restoration and dark channel theory for foggy monochrome remote sensing images with information loss. Combined with the matrix restoration based on ADMM（Alternating Direction Method of Multipliers） and dark channel theory， the restoration of remote sensing fog map under the condition of information loss is effectively realized. By combining subjective evaluation and objective evaluation， the algorithm in this paper is compared with the classical algorithm. The results show that the results obtained by the algorithm in this paper are more intuitive and visually effective， and compared with the fog map with 30% information loss， the six scenes have better visual effects. The average information entropy is improved by 1.665 2， the average peak signal-to-noise ratio is improved by 11.702 9， and the average structural similarity is improved by 0.814 6. The objective evaluation index results are excellent. Further experiments are carried out in the case of different proportions of information loss， and the results show that the clear restoration and dehazing image can still be obtained even when a large amount of information is lost.
关键词
图像去雾单色遥感图像ADMM暗通道理论
Keywords
image dehazingmonochrome remote sensing imageADMMdark channel theory
references
贺长秀.图像去雾算法研究进展［J］.现代计算机，2020（28）：47-51. doi: 10.3969/j.issn.1007-1423.2020.28.009http://dx.doi.org/10.3969/j.issn.1007-1423.2020.28.009
HE C X. Research progress on image dehazing algorithm ［J］. Modern Computer， 2020（28）： 47-51. （in Chinese）. doi: 10.3969/j.issn.1007-1423.2020.28.009http://dx.doi.org/10.3969/j.issn.1007-1423.2020.28.009
蒋华伟，杨震，张鑫，等.图像去雾算法研究进展［J］.吉林大学学报（工学版），2021，51（4）：1169-1181.
JIANG H W， YANG Z， ZHANG X， et al. Research progress of image dehazing algorithm ［J］. Journal of Jilin University （Engineering and Technology Edition）， 2021， 51（4）： 1169-1181. （in Chinese）
张登银，鞠铭烨，钱雯.图像去雾算法研究现状与展望［J］.南京邮电大学学报（自然科学版），2020，40（5）：101-111.
ZHANG D Y， JU M Y， QIAN W. Research status and prospect of image dehazing algorithm ［J］. Journal of Nanjing University of Posts and Telecommunications （Natural Science Edition）， 2020， 40（5）： 101-111. （in Chinese）
HUANG S C， CHENG F C， CHIU Y S. Efficient contrast enhancement using adaptive Gamma correction with weighting distribution ［J］. IEEE Transactions on Image Processing， 2013， 22（3）： 1032-1041. doi: 10.1109/tip.2012.2226047http://dx.doi.org/10.1109/tip.2012.2226047
SUN Z B， FENG W Q， ZHAO Q， et al. Brightness preserving image enhancement based on a gradient and intensity histogram ［J］. Journal of Electronic Imaging， 2015， 24（5）： 053006. doi: 10.1117/1.jei.24.5.053006http://dx.doi.org/10.1117/1.jei.24.5.053006
HA C， KIM W， JEONG J. Remote sensing image enhancement based on singular value decomposition ［J］. Optical Engineering， 2013， 52（8）： 083101. doi: 10.1117/1.oe.52.8.083101http://dx.doi.org/10.1117/1.oe.52.8.083101
FU X Y， WANG J Y， ZENG D L， et al. Remote sensing image enhancement using regularized-histogram equalization and DCT ［J］. IEEE Geoscience and Remote Sensing Letters， 2015， 12（11）： 2301-2305. doi: 10.1109/lgrs.2015.2473164http://dx.doi.org/10.1109/lgrs.2015.2473164
ATTA R， GHANBARI M. Low-contrast satellite images enhancement using discrete cosine transform pyramid and singular value decomposition ［J］. IET Image Processing， 2013， 7（5）： 472-483. doi: 10.1049/iet-ipr.2013.0083http://dx.doi.org/10.1049/iet-ipr.2013.0083
WANG S H， LUO G. Naturalness preserved image enhancement using a priori multi-layer lightness statistics ［J］. IEEE Transactions on Image Processing， 2018， 27（2）： 938-948. doi: 10.1109/tip.2017.2771449http://dx.doi.org/10.1109/tip.2017.2771449
何涛，赵停，徐鹤.基于暗通道先验的单幅图像去雾新算法［J］.计算机科学，2021，48（7）：219-224. doi: 10.11896/jsjkx.200700160http://dx.doi.org/10.11896/jsjkx.200700160
HE T， ZHAO T， XU H. Novel algorithm of single image dehazing based on dark channel prior ［J］. Computer Science， 2021， 48（7）： 219-224. （in Chinese）. doi: 10.11896/jsjkx.200700160http://dx.doi.org/10.11896/jsjkx.200700160
李力，胡潇，彭军.基于暗通道先验的航拍图像去雾效果优化［J］.国土资源遥感，2021，33（1）：108-114. doi: 10.6046/gtzyyg.2020056http://dx.doi.org/10.6046/gtzyyg.2020056
LI L， HU X， PENG J. Fog removal effect optimization of aerial image based on dark channel prior ［J］. Remote Sensing for Land & Resources， 2021， 33（1）： 108-114. （in Chinese）. doi: 10.6046/gtzyyg.2020056http://dx.doi.org/10.6046/gtzyyg.2020056
孙乐乐，席一帆，吕悦.基于暗通道先验的去雾改进算法［J］.电子设计工程，2021，29（4）：48-52.
SUN L L， XI Y F， LV Y. An improved algorithm of fog removal based on the prior of dark channel ［J］. Electronic Design Engineering， 2021， 29（4）： 48-52. （in Chinese）
王蓉，孙勇智，马连伟.基于改进加权融合暗通道算法的图像去雾研究［J］.浙江科技学院学报，2021，33（1）：52-58. doi: 10.3969/j.issn.1671-8798.2021.01.008http://dx.doi.org/10.3969/j.issn.1671-8798.2021.01.008
WANG R， SUN Y Z， MA L W. Research of image defogging based on improved weighted-fusion dark channel algorithm ［J］. Journal of Zhejiang University of Science and Technology， 2021， 33（1）： 52-58. （in Chinese）. doi: 10.3969/j.issn.1671-8798.2021.01.008http://dx.doi.org/10.3969/j.issn.1671-8798.2021.01.008
周文舟，范晨，胡小平，等.多尺度奇异值分解的偏振图像融合去雾算法与实验［J］.中国光学，2021，14（2）：298-306. doi: 10.37188/CO.2020-0099http://dx.doi.org/10.37188/CO.2020-0099
ZHOU W Z， FAN C， HU X P， et al. Multi-scale singular value decomposition polarization image fusion defogging algorithm and experiment ［J］. Chinese Optics， 2021， 14（2）： 298-306. （in Chinese）. doi: 10.37188/CO.2020-0099http://dx.doi.org/10.37188/CO.2020-0099
黄鹤，李昕芮，宋京，等.多尺度窗口的自适应透射率修复交通图像去雾方法［J］.中国光学，2019，12（6）：1311-1320. doi: 10.3788/co.20191206.1311http://dx.doi.org/10.3788/co.20191206.1311
HUANG H， LI X R， SONG J， et al. A traffic image dehaze method based on adaptive transmittance estimation with multi-scale window ［J］. Chinese Optics， 2019， 12（6）： 1311-1320. （in Chinese）. doi: 10.3788/co.20191206.1311http://dx.doi.org/10.3788/co.20191206.1311
李鹏飞，何小海，卿粼波，等.暗通道融合亮通道优化的夜间图像去雾算法［J］.液晶与显示，2021，36（4）：596-604. doi: 10.37188/CJLCD.2020-0208http://dx.doi.org/10.37188/CJLCD.2020-0208
LI P F， HE X H， QING L B， et al. A nighttime image dehazing algorithm optimized by dark channel fusion with bright channel ［J］. Chinese Journal of Liquid Crystals and Displays， 2021， 36（4）： 596-604. （in Chinese）. doi: 10.37188/CJLCD.2020-0208http://dx.doi.org/10.37188/CJLCD.2020-0208
DONG H， PAN J S， XIANG L， et al. Multi-scale boosted dehazing network with dense feature fusion ［C］//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle： IEEE， 2020. doi: 10.1109/cvpr42600.2020.00223http://dx.doi.org/10.1109/cvpr42600.2020.00223
PANG Y W， NIE J， XIE J， et al. BidNet： binocular image dehazing without explicit disparity estimation ［C］// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition （CVPR）. Seattle： IEEE， 2020. doi: 10.1109/cvpr42600.2020.00597http://dx.doi.org/10.1109/cvpr42600.2020.00597
ZHENG Z R， REN W Q， CAO X C， et al. Ultra-high-definition image dehazing via multi-guided bilateral learning ［C］//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville： IEEE， 2021. doi: 10.1109/cvpr46437.2021.01592http://dx.doi.org/10.1109/cvpr46437.2021.01592
RECHT B， FAZEL M， PARRILO P A. Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization ［J］. SIAM Review， 2010， 52（3）： 471-501. doi: 10.1137/070697835http://dx.doi.org/10.1137/070697835
李晖晖，鱼轮，张良，等.暗通道约束和交替方向乘子法优化的湍流图像盲复原［J］.西北工业大学学报，2018，36（1）：103-109. doi: 10.3969/j.issn.1000-2758.2018.01.015http://dx.doi.org/10.3969/j.issn.1000-2758.2018.01.015
LI H H， YU L， ZHANG L， et al. Dark channel constraint and alternated direction multiplier optimization of turbulence degraded image blind restoration ［J］. Journal of Northwestern Polytechnical University， 2018， 36（1）： 103-109. （in Chinese）. doi: 10.3969/j.issn.1000-2758.2018.01.015http://dx.doi.org/10.3969/j.issn.1000-2758.2018.01.015
高珠珠.基于暗原色先验和二阶变分模型的图像去雾［D］.青岛：青岛大学，2020.
GAO Z Z. Image dehazing based on dark primary color prior and second-order variational model ［D］. Qingdao： Qingdao University， 2020.（in Chinese）
李吉，赵丽娜，侯旭珂.通过随机排序的交替方向乘子法的矩阵恢复［J］.北京化工大学学报（自然科学版），2017，44（3）：123-128. doi: 10.13543/j.bhxbzr.2017.03.021http://dx.doi.org/10.13543/j.bhxbzr.2017.03.021
LI J， ZHAO L N， HOU X K. Matrix recovery by randomly permuted alternating direction method of multipliers （ADMM） ［J］. Journal of Beijing University of Chemical Technology （Natural Science Edition）， 2017， 44（3）： 123-128. （in Chinese）. doi: 10.13543/j.bhxbzr.2017.03.021http://dx.doi.org/10.13543/j.bhxbzr.2017.03.021
杨燕，金正猛，蒋晓连，等.保持泊松噪声图像细节的快速变分去噪算法［J］.计算机工程与应用，2016，52（20）：172-176. doi: 10.3778/j.issn.1002-8331.1412-0330http://dx.doi.org/10.3778/j.issn.1002-8331.1412-0330
YANG Y， JIN Z M， JIANG X L， et al. Fast variational algorithm based on detail preserving for Poisson noise removal ［J］. Computer Engineering and Applications， 2016， 52（20）： 172-176. （in Chinese）. doi: 10.3778/j.issn.1002-8331.1412-0330http://dx.doi.org/10.3778/j.issn.1002-8331.1412-0330
查志远.基于稀疏表示与低秩模型的图像复原算法研究［D］.南京：南京大学，2018.
ZHA Z Y. Research on image reconstruction algorithm based on sparse representation and low rank model ［D］. Nanjing： Nanjing University， 2018. （in Chinese）
陈小莉.基于交替乘子法的图像去模糊技术研究［J］.电子设计工程，2019，27（5）：189-193. doi: 10.3969/j.issn.1674-6236.2019.05.041http://dx.doi.org/10.3969/j.issn.1674-6236.2019.05.041
CHEN X L. Study of image deblurring technology based on alternate direction multiplier ［J］. Electronic Design Engineering， 2019， 27（5）： 189-193. （in Chinese）. doi: 10.3969/j.issn.1674-6236.2019.05.041http://dx.doi.org/10.3969/j.issn.1674-6236.2019.05.041
0
Views
91
下载量
0
CSCD
- L-PDFDownload
- WORDDownload
- Meta-XMLDownload
- BibTeXDownload
- EndnoteDownload
- RefMan Download
- NoteExpressDownload
- NoteFirstDownload
Publicity Resources
Related Articles
Related Author
Related Institution
- Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China 130033 Postal code：130033
- Tel：0431-86176059 Email：yjxs@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.
