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基于深度学习的高噪声图像去噪算法

盖杉 鲍中运

盖杉, 鲍中运.基于深度学习的高噪声图像去噪算法.自动化学报, 2020, 46(12): 2672−2680 doi: 10.16383/j.aas.c180271
引用本文: 盖杉, 鲍中运.基于深度学习的高噪声图像去噪算法.自动化学报, 2020, 46(12): 2672−2680 doi: 10.16383/j.aas.c180271
Gai Shan, Bao Zhong-Yun. High noise image denoising algorithm based on deep learning. Acta Automatica Sinica, 2020, 46(12): 2672−2680 doi: 10.16383/j.aas.c180271
Citation: Gai Shan, Bao Zhong-Yun. High noise image denoising algorithm based on deep learning. Acta Automatica Sinica, 2020, 46(12): 2672−2680 doi: 10.16383/j.aas.c180271

基于深度学习的高噪声图像去噪算法

doi: 10.16383/j.aas.c180271
基金项目: 

国家自然科学基金 61563037

江西省杰出青年人才资助计划 20171BCB23057

详细信息
    作者简介:

    鲍中运 南昌航空大学信息工程学院硕士研究生.主要研究方向为图像处理, 模式识别, 计算机视觉, 深度学习. E-mail: baozhongyun1234@163.com

    通讯作者:

    盖杉  南昌航空大学信息工程学院副教授.主要研究方向为图像处理, 模式识别, 计算机视觉, 人工智能.本文通信作者. E-mail: gaishan@nchu.edu.cn

  • 本文责任编委  杨健

High Noise Image Denoising Algorithm Based on Deep Learning

Funds: 

National Natural Science Foundation of China 61563037

Outstanding Youth Funding Scheme of Jiangxi Province 20171BCB23057

More Information
    Author Bio:

    BAO Zhong-Yun   Master student at the School of Information Engineering, Nanchang Hangkong University. His research interest covers image processing, pattern recognition, computer vision, and deep learning

    Corresponding author: GAI Shan   Associate professor at the School of Information Engineering, Nanchang Hangkong University. His research interest covers image processing, pattern recognition, computer vision, and artificial intelligence. Corresponding author of this paper
  • Recommended by Associate Editor YANG Jian
  • 摘要: 为了更有效地实现高噪声环境下的图像去噪, 本文提出一种基于深度学习的高噪声图像去噪算法.该算法首先采用递增扩充卷积并且融合批量标准化和Leaky ReLU函数对输入含噪图像进行特征提取与学习; 然后通过结合递减扩充卷积和ReLU函数对提取的特征进行图像重构; 最后通过整合残差学习和批量标准化的端到端网络实现图像与噪声的有效分离.实验结果表明, 本文提出的算法不仅能够有效地去除高噪声环境下的图像噪声, 获得更高的峰值信噪比(Peak signal-to-noise ratio, PSNR)与结构相似度(Structural similarity index, SSIM), 而且还能够有效地改善图像的视觉效果, 具有较好的实用性.
    Recommended by Associate Editor YANG Jian
    1)  本文责任编委  杨健
  • 图  1  本文模型结构示意图

    Fig.  1  The structure of the proposed model

    图  2  不同网络结构模型训练收敛图

    Fig.  2  training convergence graph of different network

    图  3  不同的去噪方法对lighthouse的去噪效果对比图

    Fig.  3  Comparison of denoising effects of lighthouse with different denoising methods

    图  4  不同的去噪方法对butterfly的去噪效果对比图

    Fig.  4  Comparison of denoising effects of butterfly with different denoising methods

    表  1  不同去噪算法在BSD68数据集下的峰值信噪比(PSNR) (dB)

    Table  1  The PSNR value using different denoising algorithms at the BSD68 data set (dB)

    $\sigma$ BM3D WNNM MLP TNRD DnCNN EPLL CSF 特定噪声模型 随机噪声模型
    15 31.07 31.37 31.42 31.73 31.21 31.24 31.94 31.85
    25 28.57 28.83 28.96 28.92 29.23 28.68 28.74 29.46 29.38
    40 26.22 26.33 26.49 26.88 26.26 26.30 27.11 27.06
    50 25.62 25.87 26.03 25.97 26.23 25.67 26.48 26.47
    60 23.18 23.55 23.43 23.73 23.24 23.27 24.01 24.06
    下载: 导出CSV

    表  2  不同去噪算法在BSD68数据集下的结构相似度

    Table  2  The SSIM value using different denoising algorithms at the BSD68 data set

    $\sigma$ BM3D WNNM MLP TNRD DnCNN 本文方法1 本文方法2
    15 0.8772 0.8774 0.8792 0.8826 0.8826 0.8831 0.8827
    25 0.8017 0.8019 0.8120 0.8157 0.8190 0.8193 0.8190
    40 0.7223 0.7237 0.7294 0.7310 0.7322 0.7334 0.7331
    50 0.6869 0.6871 0.6956 0.7029 0.7076 0.7102 0.7100
    60 0.6521 0.6544 0.6643 0.6712 0.6745 0.6796 0.6799
    下载: 导出CSV

    表  3  不同尺寸大小的测试图像去噪运行时间比较($\sigma = 25$) (s)

    Table  3  The running time of test images denoising with different size ($\sigma = 25$) (s)

    图像块大小(像素) 配置 BM3D WNNM TNRD MLP EPLL CSF DnCNN 特定噪声模型 随机噪声模型
    $256\times 256$ CPU/GPU 0.65 203.1 0.45/0.010 1.42 25.4 2.11/– 0.74/0.014 0.68/0.016 0.97/0.020
    $512\times 512$ CPU/GPU 2.85 773.2 1.33/0.032 5.51 45.5 5.67/0.92 3.41/0.051 2.98/0.072 3.68/0.083
    $1 024\times 1 024$ CPU/GPU 11.89 2 536.4 4.61/0.116 19.4 422.1 40.8/1.72 12.1/0.200 10.7/0.160 13.7/0.173
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-05-02
  • 录用日期:  2019-01-09
  • 刊出日期:  2020-12-29

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