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基于主成分分析的分块视频噪声估计

肖进胜 朱力 赵博强 雷俊锋 王莉

肖进胜, 朱力, 赵博强, 雷俊锋, 王莉. 基于主成分分析的分块视频噪声估计. 自动化学报, 2018, 44(9): 1618-1625. doi: 10.16383/j.aas.2017.c160764
引用本文: 肖进胜, 朱力, 赵博强, 雷俊锋, 王莉. 基于主成分分析的分块视频噪声估计. 自动化学报, 2018, 44(9): 1618-1625. doi: 10.16383/j.aas.2017.c160764
XIAO Jin-Sheng, ZHU Li, ZHAO Bo-Qiang, LEI Jun-Feng, WANG Li. Block-based Video Noise Estimation Algorithm via Principal Component Analysis. ACTA AUTOMATICA SINICA, 2018, 44(9): 1618-1625. doi: 10.16383/j.aas.2017.c160764
Citation: XIAO Jin-Sheng, ZHU Li, ZHAO Bo-Qiang, LEI Jun-Feng, WANG Li. Block-based Video Noise Estimation Algorithm via Principal Component Analysis. ACTA AUTOMATICA SINICA, 2018, 44(9): 1618-1625. doi: 10.16383/j.aas.2017.c160764

基于主成分分析的分块视频噪声估计

doi: 10.16383/j.aas.2017.c160764
基金项目: 

湖北省自然科学基金 2016CFB499

国家自然科学基金 61471272

详细信息
    作者简介:

    肖进胜  博士, 武汉大学电子信息学院副教授.2001年于武汉大学获得理学博士学位.主要研究方向为视频图像处理, 计算机视觉, 视觉感知增强.E-mail:xiaojs@whu.edu.cn

    朱力  武汉大学电子信息学院硕士研究生.2016年获得西安邮电大学电子信息工程学士学位.主要研究方向为计算机视觉.E-mail:zhul@whu.edu.cn

    赵博强  武汉大学电子信息学院硕士研究生.2015年获得河南工业大学电子信息工程学士学位.主要研究方向为计算机视觉.E-mail:zbq@whu.edu.cn

    王莉  博士, 烽火通信科技股份有限公司项目经理.2011年获得武汉大学博士学位.主要研究方向为图像处理与光纤通信.E-mail:wl@fiberhome.com.cn

    通讯作者:

    雷俊锋  博士, 武汉大学电子信息学院副教授.2002年于武汉大学获得理学博士学位.主要研究方向为图像处理与计算视觉成像.本文通信作者.E-mail:jflei@whu.edu.cn

Block-based Video Noise Estimation Algorithm via Principal Component Analysis

Funds: 

Natural Science Foundation of Hubei Province 2016CFB499

National Natural Science Foundation of China 61471272

More Information
    Author Bio:

     Ph. D., associate professor at the School of Electronic Information, Wuhan University. His research interest covers video and image processing, computer vision, and visual perception enhancement

     Master student at the School of Electronic Information, Wuhan University. He received his bachelor degree from Xi'an University of Posts and Telecommunications in 2016. His main research interest is computer vision

     Master student at the School of Electronic Information, Wuhan University. He received his bachelor degree from He'nan University of Technology in 2015. His main research interest is computer vision

     Ph. D., project manager in the FiberHome Telecommunication Technologies Co., Ltd. She received her Ph. D. degree from Wuhan University in 2011. Her research interest covers image processing and fibre communication

    Corresponding author: LEI Jun-Feng  Ph. D., associate professor at the School of Electronic Information, Wuhan University. His research interest covers image processing, and computational vision imaging. Corresponding author of this paper
  • 摘要: 噪声估计在视频去噪领域具有重要的研究意义.实际生活中的噪声都是未知的,然而现存的视频去噪算法通常都假定视频的噪声水平是已知的,本文提出一种基于主成分分析(Principal component analysis,PCA)的分块视频噪声估计算法.首先,基于帧间进行块匹配寻找相似块,得到差分图像以消除视频运动的影响;其次,将正态分布函数作为阈值函数简化噪声估计算法模型;最后,设置明确迭代指标使得估计的结果更加精确,且降低了计算复杂度.主观视觉效果和客观指标对比表明,本文提出的基于主成分分析的分块视频噪声估计算法比其他优秀的噪声估计算法误差小同时鲁棒性高,能准确地估计视频噪声.
    1)  本文责任编委 桑农
  • 图  1  Gamma概率密度图

    Fig.  1  Gamma probability density function

    图  2  在低噪声强度下估计效果对比

    Fig.  2  Comparisons in low noise case

    图  3  在高噪声强度下估计效果对比

    Fig.  3  Comparisons in high noise case

    图  4  本文算法迭代噪声水平估计流程图

    Fig.  4  Flowchart of the iterative noise level estimation for proposed algorithm

    图  5  加噪20 dB视频序列(Flower, Football)估计误差

    Fig.  5  Noise estimation error for 20 dB noisy sequences (Flower, Football)

    图  6  加噪30 dB视频序列(Flower, Football)估计误差

    Fig.  6  Noise estimation error for 30 dB noisy sequences (Flower, Football)

    图  7  加噪40 dB视频序列(Flower, Football)估计误差

    Fig.  7  Noise estimation error for 40 dB noisy sequences (Flower, Football)

    表  1  不同分布函数噪声估计对比

    Table  1  Comparison of noise estimation for different function

    Noise level (dB) Lena Akiyo Bus Coastguard
    ${{\sigma }_{n}}=10$ Liu等[14] 9.68 9.88 9.69 9.79
    ${{\sigma }_{n}}=10$ Proposed 9.86 9.79 9.97 9.93
    ${{\sigma }_{n}}=20$ Liu等[14] 19.56 19.61 19.64 19.67
    ${{\sigma }_{n}}=20$ Proposed 19.72 19.65 19.78 19.74
    ${{\sigma }_{n}}=30$ Liu等[14] 29.54 29.54 29.13 29.59
    ${{\sigma }_{n}}=30$ Proposed 29.65 29.81 29.50 29.67
    ${{\sigma }_{n}}=40$ Liu等[14] 38.43 39.34 38.99 39.37
    ${{\sigma }_{n}}=40$ Proposed 39.50 39.38 39.64 39.60
    下载: 导出CSV

    表  2  VBM3D、PID、VBM4D和本文算法的PSNR和SSIM对比

    Table  2  The comparisons of PSNR and SSIM results of VBM3D, PID, VBM4D and proposed algorithm

    Noise level (dB) Algorithm Akiyo PSNR/SSIM Mobile PSNR/SSIM Flowergarden PSNR/SSIM Foreman PSNR/SSIM Football PSNR/SSIM
    ${{\sigma }_{n}}=10$ VBM3D 35.488/0.877 32.374/0.954 34.250/0.984 34.313/0.902 33.048/0.951
    ${{\sigma }_{n}}=10$ PID 31.396/0.763 29.538/0.917 31.276/0.962 31.094/0.844 30.048/0.921
    ${{\sigma }_{n}}=10$ VBM4D 30.290/0.730 29.727/0.915 29.972/0.842 30.078/0.813 29.833/0.923
    ${{\sigma }_{n}}=10$ Proposed 37.842/0.944 32.454/0.972 33.675/0.983 36.295/0.938 33.488/0.960
    ${{\sigma }_{n}}=20$ VBM3D 30.239 /0.715 27.653/0.897 29.444/0.961 29.587/0.786 28.171/0.868
    ${{\sigma }_{n}}=20$ PID 26.011/0.836 24.572/0.833 25.945/0.896 25.937/0.675 24.889/0.793
    ${{\sigma }_{n}}=20$ VBM4D 29.594/0.767 27.249/0.898 27.599/0.852 28.904/0.797 27.727/0.885
    ${{\sigma }_{n}}=20$ Proposed 34.986/0.925 28.322/0.936 29.386/0.957 33.098/0.878 29.890/0.894
    ${{\sigma }_{n}}=30$ VBM3D 27.463/0.609 25.025/0.853 26.611/0.932 26.934/0.698 25.406/0.783
    ${{\sigma }_{n}}=30$ PID 23.051/0.815 21.803/0.769 22.895/0.825 23.017/0.565 21.929/0.676
    ${{\sigma }_{n}}=30$ VBM4D 29.026/0.780 25.884/0.883 26.185/0.848 28.208/0.782 26.557/0.842
    ${{\sigma }_{n}}=30$ Proposed 32.579/0.866 26.045/0.898 27.098/0.932 31.379/0.835 27.995/0.825
    下载: 导出CSV
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  • 收稿日期:  2016-11-10
  • 录用日期:  2017-06-12
  • 刊出日期:  2018-09-20

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