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基于的DTV图像去噪模型

庞志峰 张慧丽 史宝丽

庞志峰, 张慧丽, 史宝丽等 . 基于的DTV图像去噪模型[J]. 北京航空航天大学学报, 2019, 45(3): 464-471. doi: 10.13700/j.bh.1001-5965.2018.0329
引用本文: 庞志峰, 张慧丽, 史宝丽等 . 基于的DTV图像去噪模型[J]. 北京航空航天大学学报, 2019, 45(3): 464-471. doi: 10.13700/j.bh.1001-5965.2018.0329
PANG Zhifeng, ZHANG Huili, SHI Baoliet al. Image denoising model based on directional total variation[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(3): 464-471. doi: 10.13700/j.bh.1001-5965.2018.0329(in Chinese)
Citation: PANG Zhifeng, ZHANG Huili, SHI Baoliet al. Image denoising model based on directional total variation[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(3): 464-471. doi: 10.13700/j.bh.1001-5965.2018.0329(in Chinese)

基于的DTV图像去噪模型

doi: 10.13700/j.bh.1001-5965.2018.0329
基金项目: 

国家"973"计划 2015CB856003

国家自然科学基金 11401170

国家自然科学基金 U1304610

工程数学建模与分析湖南省重点实验室开放基金 Changsha University of Science and Technology

河南大学优青培育项目 yqpy20170062

详细信息
    作者简介:

    庞志峰  男, 博士, 副教授。主要研究方向:图像处理

    张慧丽  女, 硕士研究生。主要研究方向:图像处理

    史宝丽  女, 博士, 副教授。主要研究方向:图像处理

    通讯作者:

    庞志峰, E-mail:zhifengpang@163.com

  • 中图分类号: TP391.4

Image denoising model based on directional total variation

Funds: 

National Basic Research Program of China 2015CB856003

National Natural Science Foundation of China 11401170

National Natural Science Foundation of China U1304610

Engineering Mathematical Modeling and Analysis, Foundation for Key Laboratory of Hunan Province Changsha University of Science and Technology

Henan University Excellent Youth Cultivation Project yqpy20170062

More Information
  • 摘要:

    针对纹理图像的去噪问题,通过分析全变分(TV)去噪模型与方向全变分(DTV)去噪模型,提出了一种具有鲁棒性的基于的DTV去噪模型。为了刻画图像中的不同结构特征,该模型中DTV正则项的指数p由图像的结构来确定在(0,2)中自适应地选取。由于该模型是含有可分性算子的非光滑优化问题,可用交替方向乘子法(ADMM)求解,并能保证算法的收敛性。数值实验结果表明:与其他经典模型相比,提出的模型取得了更高的峰值信噪比和结构相似度,在去除噪声的同时能有效保持图像的细节信息。

     

  • 图 1  五点差分格式

    Figure 1.  Five-point difference format

    图 2  两个差分方向取相同权重时的旋转变换

    Figure 2.  Rotation transformation when two differential directions take the same weight

    图 3  两个差分方向取不同权重时的旋转变换

    Figure 3.  Rotational transformation when two differential directions take different weights

    图 4  仿真实验的原始图像

    Figure 4.  Original image of simulation experiment

    图 5  DTVP模型去噪过程中SNR随参数p的变化

    Figure 5.  Variation of SNR with p during denoising by DTVP model

    图 6  不同去噪模型去噪后的残差图像比较

    Figure 6.  Compare residual image after denoising by different denoising models

    图 7  不同去噪模型去噪后的复原图像比较

    Figure 7.  Compare restored images after denoising by different denoising models

    图 8  不同去噪模型去噪后的直方图比较

    Figure 8.  Compare histograms after denoising by different denoising models

    表  1  不同去噪模型去噪后所得的SNR和SSIM比较

    Table  1.   Comparison of SNR and SSIM obtained after denoising by different denoising models

    图像 模型 SNR/dB SSIM
    σ=0.01 σ=0.05 σ=0.1 σ=0.01 σ=0.05 σ=0.1
    图 4(a) ROF 18.846 5 14.873 3 13.415 7 0.757 9 0.461 0 0.332 2
    DTV 19.823 9 15.971 4 14.301 1 0.792 0 0.554 5 0.428 4
    ADMM-DTV 19.896 0 16.060 4 14.381 1 0.796 2 0.560 1 0.438 9
    DTVP 19.923 1 16.219 2 14.607 0 0.800 0 0.591 5 0.484 1
    图 4(b) ROF 21.350 5 19.210 5 18.414 8 0.652 1 0.511 8 0.462 0
    DTV 21.530 8 19.344 0 18.478 3 0.673 2 0.532 0 0.483 0
    ADMM-DTV 21.804 4 19.842 5 19.149 4 0.682 2 0.553 5 0.508 3
    DTVP 22.024 1 20.224 6 19.509 4 0.701 4 0.578 0 0.534 2
    图 4(c) ROF 19.094 0 12.534 0 09.684 7 0.982 6 0.922 5 0.854 2
    DTV 19.314 8 12.755 5 9.880 8 0.983 5 0.926 2 0.860 4
    ADMM-DTV 19.537 1 12.908 6 10.054 3 0.984 3 0.928 6 0.865 5
    DTVP 20.251 9 13.567 8 10.446 6 0.986 6 0.938 3 0.876 1
    图 4(d) ROF 19.428 8 14.903 9 13.196 0 0.851 0 0.642 0 0.486 9
    DTV 20.483 1 15.805 8 13.887 4 0.884 4 0.703 9 0.573 8
    ADMM-DTV 20.699 0 16.016 1 14.011 2 0.889 1 0.712 0 0.586 4
    DTVP 20.925 7 16.325 7 14.357 3 0.896 1 0.740 7 0.622 7
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-06-04
  • 录用日期:  2018-09-03
  • 网络出版日期:  2019-03-20

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