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基于学习字典的机器人图像稀疏表示方法

郭俊锋 李育亮

郭俊锋, 李育亮. 基于学习字典的机器人图像稀疏表示方法. 自动化学报, 2020, 46(4): 820-830. doi: 10.16383/j.aas.2018.c170352
引用本文: 郭俊锋, 李育亮. 基于学习字典的机器人图像稀疏表示方法. 自动化学报, 2020, 46(4): 820-830. doi: 10.16383/j.aas.2018.c170352
GUO Jun-Feng, LI Yu-Liang. Sparse Representation of Robot Image Based on Dictionary Learning Algorithm. ACTA AUTOMATICA SINICA, 2020, 46(4): 820-830. doi: 10.16383/j.aas.2018.c170352
Citation: GUO Jun-Feng, LI Yu-Liang. Sparse Representation of Robot Image Based on Dictionary Learning Algorithm. ACTA AUTOMATICA SINICA, 2020, 46(4): 820-830. doi: 10.16383/j.aas.2018.c170352

基于学习字典的机器人图像稀疏表示方法

doi: 10.16383/j.aas.2018.c170352
基金项目: 

国家自然科学基金 51465034

详细信息
    作者简介:

    郭俊锋  兰州理工大学机电工程学院副教授.主要研究方向为先进控制技术, 信号检测, 压缩感知.E-mail:junf_guo@163.com

    通讯作者:

    李育亮  兰州理工大学硕士研究生.主要研究方向为机器人图像处理, 稀疏表示算法.本文通信作者.E-mail: lyl931206@163.com

Sparse Representation of Robot Image Based on Dictionary Learning Algorithm

Funds: 

National Natural Science Foundation of China 51465034

More Information
    Author Bio:

    GUO Jun-Feng   Associate professor at the College of mechanical and electrical engineering, Lanzhou University of Technology. His research interest covers advanced control technology, signal detection, and compression sensing

    Corresponding author: LI Yu-Liang  Master student at the College of mechanical and electrical engineering, Lanzhou University of Technology. His research interest covers robot image processing and sparse representation algorithm. Corresponding author of this paper
  • 摘要: 针对机器人图像压缩感知(Compressed sensing, CS)过程中稀疏字典训练时间过长的问题, 本文提出了一种更加高效的字典学习方法.通过对MOD、K-SVD、SGK等字典学习算法研究, 从参与更新的字典原子列数入手, 将残差项变形为多列原子同时更新, 进而利用最小二乘法连续地更新字典中的多个原子.本文算法是对SGK算法字典学习效率的进一步提高, 减少了单次迭代的计算量, 加快了字典学习速度.实验表明, 本文算法与K-SVD和SGK算法相比, 在字典稀疏性和重构图像质量变化很小的情况下, 字典训练时间得到较明显缩短.
    Recommended by Associate Editor LI Ming
    1)  本文责任编委 黎铭
  • 图  1  本文字典学习算法流程图

    Fig.  1  The flow chart of the dictionary learning algorithm in this paper

    图  2  原始图像及其加噪图像

    Fig.  2  Original image and noisy image

    图  3  重构效果对比图

    Fig.  3  The reconstruction effect comparison

    图  4  不同迭代次数下的字典学习时间

    Fig.  4  Time consumption of dictionary learning for different iteration times

    图  5  不同噪声程度下的字典学习时间

    Fig.  5  Time consumption of dictionary learning for different noise levels

    图  6  自然场景图像重构效果对比

    Fig.  6  Comparison of image reconstruction effects in natural scenes

    表  1  不同字典原子选取方式效果对比

    Table  1  Comparison of the effects for different selections of dictionary atoms

    选取待更新字典原子的方式 顺次选取连续的2列 随机选取连续的2列 随机选取离散的2列
    字典学习时间(s) 3.994 5.268 6.788
    峰值信噪比PSNR (dB) 32.092 31.968 31.866
    平均结构相似度MSSIM 0.3183 0.3152 0.3114
    下载: 导出CSV

    表  2  同时更新不同字典列数效果对比

    Table  2  Effect comparison of updating different numbers of dictionary columns at the same time

    同时更新原子列数 2 4 8 16 32 64
    字典学习时间(s) 4.117 4.194 3.630 3.744 4.032 4.093
    峰值信噪比PSNR (dB) 32.092 32.065 32.081 32.145 32.058 32.045
    平均结构相似度MSSIM 0.3183 0.3176 0.3177 0.3173 0.3164 0.3181
    下载: 导出CSV

    表  3  不同算法的重构效果

    Table  3  Reconstruction effect with different algorithms

    算法 DCT字典 K-SVD字典 SGK字典 本文算法字典
    字典学习时间(s) -- 20.079 4.254 3.605
    峰值信噪比PSNR (dB) 30.915 31.950 31.935 31.947
    平均结构相似度MSSIM 0.3022 0.3149 0.3137 0.3144
    下载: 导出CSV

    表  4  不同迭代次数重构图像质量

    Table  4  The quality of the reconstructed image with different numbers of iteration

    迭代次数 K-SVD字典 SGK字典 本文算法字典
    PSNR (dB) MSSIM PSNR (dB) MSSIM PSNR (dB) MSSIM
    3 31.4559 0.30762 31.4459 0.30779 31.4603 0.30825
    6 31.9951 0.31384 31.9291 0.31171 31.9265 0.31207
    9 31.9704 0.31386 31.9490 0.31287 31.9194 0.31313
    12 32.2901 0.31418 32.2661 0.31423 32.2416 0.31279
    15 32.3571 0.31986 32.3297 0.31898 32.2724 0.31921
    18 32.4407 0.32208 32.3781 0.32182 32.3485 0.32141
    21 32.4489 0.32209 32.4040 0.32206 32.3971 0.32172
    下载: 导出CSV

    表  5  不同噪声水平下重构图像质量

    Table  5  The quality of the reconstructed image with different noise levels

    标准差 K-SVD字典 SGK字典 本文算法字典
    PSNR (dB) MSSIM PSNR (dB) MSSIM PSNR (dB) MSSIM
    10 35.9741 0.45381 35.9440 0.45390 35.9620 0.45430
    20 33.2254 0.34207 33.1664 0.34219 33.2006 0.34282
    30 31.3615 0.30139 31.2992 0.29996 31.3157 0.29984
    40 29.4226 0.26534 29.3889 0.26387 29.4074 0.26427
    50 28.0932 0.23598 28.0699 0.23470 28.0908 0.23485
    60 26.8134 0.20862 26.8110 0.20815 26.8230 0.20744
    70 25.6137 0.18573 25.6164 0.18493 25.6238 0.18591
    下载: 导出CSV

    表  6  不同图像信号的重构效果

    Table  6  Reconstruction effect of different image signals

    图像 算法 时间(s) MSSIM PSNR (dB)
    lena K-SVD 18.343162 0.27010 25.8505
    SGK 3.891636 0.26863 25.8154
    本文算法 3.102337 0.26916 25.8230
    peppers K-SVD 19.244135 0.35640 26.1407
    SGK 4.141303 0.35574 26.1445
    本文算法 3.092842 0.35545 26.1353
    barbara K-SVD 19.438234 0.35642 25.2067
    SGK 4.114145 0.35763 25.1997
    本文算法 3.197942 0.35774 25.2009
    下载: 导出CSV

    表  7  不同场景自然图像信号的重构效果

    Table  7  Reconstruction effect of image signals in different natural scenes

    图像 时间(s) MSSIM PSNR (dB)
    K-SVD SGK 本文算法 K-SVD SGK 本文算法 K-SVD SGK 本文算法
    (a)明 20.196063 4.470679 3.958860 0.35006 0.35036 0.35019 26.3382 26.3285 26.3339
    (a)暗 19.138866 4.303163 3.779787 0.25134 0.25155 0.25157 27.0640 27.0637 27.0611
    (b)视角1 19.294378 4.068634 3.263159 0.29148 0.29095 0.29066 25.9845 25.9772 29.9781
    (b)视角2 21.498433 4.167165 3.233882 0.30110 0.30121 0.30176 28.2839 28.2803 28.2971
    (c)清晰 20.856516 4.534554 4.031721 0.35926 0.35839 0.35854 26.0795 26.0734 26.0601
    (c)模糊 18.319955 3.874162 2.932584 0.41004 0.41013 0.40993 29.4348 29.4386 29.4385
    下载: 导出CSV
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  • 收稿日期:  2017-06-26
  • 录用日期:  2018-03-06
  • 刊出日期:  2020-04-24

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