2.765

2022影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于高斯权重衰减的迭代优化去雾算法

杨燕 陈高科 周杰

杨燕, 陈高科, 周杰. 基于高斯权重衰减的迭代优化去雾算法. 自动化学报, 2019, 45(4): 819-828. doi: 10.16383/j.aas.c170369
引用本文: 杨燕, 陈高科, 周杰. 基于高斯权重衰减的迭代优化去雾算法. 自动化学报, 2019, 45(4): 819-828. doi: 10.16383/j.aas.c170369
YANG Yan, CHEN Gao-Ke, ZHOU Jie. Iterative Optimization Defogging Algorithm Using Gaussian Weight Decay. ACTA AUTOMATICA SINICA, 2019, 45(4): 819-828. doi: 10.16383/j.aas.c170369
Citation: YANG Yan, CHEN Gao-Ke, ZHOU Jie. Iterative Optimization Defogging Algorithm Using Gaussian Weight Decay. ACTA AUTOMATICA SINICA, 2019, 45(4): 819-828. doi: 10.16383/j.aas.c170369

基于高斯权重衰减的迭代优化去雾算法

doi: 10.16383/j.aas.c170369
基金项目: 

甘肃省财政厅基本科研业务费基金 214138

兰州交通大学教改项目 160012

国家自然科学基金 61561030

详细信息
    作者简介:

    陈高科  兰州交通大学电子与信息工程学院硕士研究生.主要研究方向为数字图像处理.E-mail:kk_325@outlook.com

    周杰  兰州交通大学电子与信息工程学院硕士研究生.主要研究方向为数字图像处理.E-mail:zhouj5239@wuhanrt.com

    通讯作者:

    杨燕  兰州交通大学电子与信息工程学院教授.主要研究方向为数字图像处理, 智能信息处理, 语音信号处理.本文通信作者.E-mail:yangyantd@mail.lzjtu.cn

Iterative Optimization Defogging Algorithm Using Gaussian Weight Decay

Funds: 

Research Fund of Department of Finance of Gansu Province 214138

Research Fund of Teaching Reform Project of Lanzhou Jiaotong University 160012

National Natural Science Foundation of China 61561030

More Information
    Author Bio:

      Master student at the School of Electronic and Information Engineering, Lanzhou Jiaotong University. His main research interest is digital image processing

      Master student at the School of Electronic and Information Engineering, Lanzhou Jiaotong University. His research interest covers digital image processing

    Corresponding author: YANG Yan   Professor at the School of Electronic and Information Engineering, Lanzhou Jiaotong University. Her research interest covers digital image processing, intelligent information processing, and voice signal processing. Corresponding author of this paper
  • 摘要: 针对暗通道先验算法最小滤波使用的不足,提出一种基于高斯权重衰减的迭代优化去雾方法.该方法首先利用Kirsch算子滤波构造高斯函数逼近暗通道操作,然后用交叉双边滤波消除纹理效应,其次,在透射率为最优的前提下,利用高斯暗通道来简化大气散射模型,从而得到粗略透射率;为了得到最优透射率,使用Kirsch和Laplacian算子构成的一组高阶滤波器进行迭代处理,从而获得最优效果;最后,结合大气散射模型复原无雾图像.通过大量实验测试验证,所提假设成立,复原的图像细节明显,明亮度适宜,并且在客观评价中也体现出了优势.
    1)  本文责任编委 左旺孟
  • 图  1  本文算法流程图

    Fig.  1  The flowchart of our algorithm

    图  2  本文算法实现过程效果图

    Fig.  2  The process effect map of our algorithm

    图  3  无雾图像高斯暗通道灰度级分布

    Fig.  3  Gaussian dark channel grayscale distribution of dehazed images

    图  4  局部大气光过程效果图

    Fig.  4  The process map of local atmospheric light estimation

    图  5  高阶滤波器组(中心为Laplacian算子, 周围为Kirsch算子)

    Fig.  5  High order filter bank (Laplacian center, Kirsch operator around)

    图  6  透射率迭代优化过程效果图

    Fig.  6  The transmission iterative optimization process map

    图  7  实验效果对比

    Fig.  7  Comparison of experimental results

    图  8  假设验证效果图

    Fig.  8  The effect image of validate the hypothesis

    图  9  客观评价(实验图像为图 7 (a)图像, 从左到右依次为图 1~4)

    Fig.  9  Objective evaluation (experimental images as shown in Fig. 7 (a) image, followed by left to right for Figs. 1~4)

  • [1] Xu Y, Wen J, Fei L K, Zhang Z. Review of video and image defogging algorithms and related studies on image restoration and enhancement. In:Proceedings of the IEEE 2016 Access. 2016, 4:165-188
    [2] 杨燕, 陈高科.基于光补偿和逐像素透射率的图像复原算法.通信学报, 2017, 38(5):48-56 http://d.old.wanfangdata.com.cn/Periodical/txxb201705006

    Yang Yan, Chen Gao-Ke. Single image visibility restoration using optical compensation and pixel-by-pixel transmission estimation. Journal on Communications, 2017, 38(5):48-56 http://d.old.wanfangdata.com.cn/Periodical/txxb201705006
    [3] He L Y, Zhao J Z, Zheng N N, Bi D Y. Haze removal using the difference-structure-preservation prior. IEEE Transactions on Image Processing, 2017, 26(3):1063-1075 doi: 10.1109/TIP.2016.2644267
    [4] He K M, Sun J, Tang X O. Single image haze removal using dark channel prior. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(12):2341-2353 doi: 10.1109/TPAMI.2010.168
    [5] Ancuti C O, Ancuti C. Single image dehazing by multi-scale fusion. IEEE Transactions on Image Processing, 2013, 22(8):3271-3282 doi: 10.1109/TIP.2013.2262284
    [6] Tan R T. Visibility in bad weather from a single image. In:Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, AK, USA:IEEE, 2008. 1-8
    [7] Fattal R. Single image dehazing. ACM Transactions on Graphics, 2008, 27(3):Article No. 72
    [8] Meng G F, Wang Y, Duan J Y, Xiang S M, Pan C H. Efficient image dehazing with boundary constraint and contextual regularization. In:Proceedings of the 2013 IEEE International Conference on Computer Vision. Sydney, NSW, Australia:IEEE, 2013. 617-624
    [9] Sun W, Wang H, Sun C H, Guo B L, Jia W Y, Sun M G. Fast single image haze removal via local atmospheric light veil estimation. Computers and Electrical Engineering, 2015, 46:371-383 doi: 10.1016/j.compeleceng.2015.02.009
    [10] He K M, Sun J, Tang X O. Guided image filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6):1397-1409 doi: 10.1109/TPAMI.2012.213
    [11] Sulami M, Glatzer I, Fattal R, Werman M. Automatic recovery of the atmospheric light in hazy images. In:Proceedings of the 2014 IEEE International Conference on Computational Photography. Santa Clara, CA, USA:IEEE, 2014:1-11
    [12] Zhu Q S, Mai J M, Shao L. A fast single image haze removal algorithm using color attenuation prior. IEEE Transactions on Image Processing, 2015, 24(11):3522-3533 doi: 10.1109/TIP.2015.2446191
    [13] Cai B L, Xu X M, Jia K, Qing C M, Tao D C. DehazeNet:an end-to-end system for single image haze removal. IEEE Transactions on Image Processing, 2016, 25(11):5187-5198 doi: 10.1109/TIP.2016.2598681
    [14] Ren W Q, Liu S, Zhang H, Pan J S, Cao X C, Yang M H. Single image dehazing via multi-scale convolutional neural networks. In:Proceedings of the 2016 Computer Vision, Lecture Notes in Computer Science, 2016, 9906:154-164
    [15] Wang Z, Bovik A C, Sheikh H R. Structural similarity based image quality assessment. Digital Video Image Quality and Perceptual Coding. Boca Raton, FL:CRC Press, 2005.
    [16] Hautiére N, Tarel J P, Aubert D, Dumont É. Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Analysis and Stereology Journal, 2008, 27(2):87-95 http://d.old.wanfangdata.com.cn/OAPaper/oai_doaj-articles_320fb103b4a9eea153bb293dd73e7f82
  • 加载中
图(9)
计量
  • 文章访问数:  2289
  • HTML全文浏览量:  370
  • PDF下载量:  424
  • 被引次数: 0
出版历程
  • 收稿日期:  2017-07-06
  • 录用日期:  2018-01-20
  • 刊出日期:  2019-04-20

目录

    /

    返回文章
    返回