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

基于最大加权投影求解的彩色图像灰度化对比度保留算法

卢红阳 刘且根 熊娇娇 王玉皞 邓晓华

卢红阳, 刘且根, 熊娇娇, 王玉皞, 邓晓华. 基于最大加权投影求解的彩色图像灰度化对比度保留算法. 自动化学报, 2017, 43(5): 843-854. doi: 10.16383/j.aas.2017.c160077
引用本文: 卢红阳, 刘且根, 熊娇娇, 王玉皞, 邓晓华. 基于最大加权投影求解的彩色图像灰度化对比度保留算法. 自动化学报, 2017, 43(5): 843-854. doi: 10.16383/j.aas.2017.c160077
LU Hong-Yang, LIU Qie-Gen, XIONG Jiao-Jiao, WANG Yu-Hao, DENG Xiao-Hua. Maximum Weighted Projection Solver for Contrast Preserving Decolorization. ACTA AUTOMATICA SINICA, 2017, 43(5): 843-854. doi: 10.16383/j.aas.2017.c160077
Citation: LU Hong-Yang, LIU Qie-Gen, XIONG Jiao-Jiao, WANG Yu-Hao, DENG Xiao-Hua. Maximum Weighted Projection Solver for Contrast Preserving Decolorization. ACTA AUTOMATICA SINICA, 2017, 43(5): 843-854. doi: 10.16383/j.aas.2017.c160077

基于最大加权投影求解的彩色图像灰度化对比度保留算法

doi: 10.16383/j.aas.2017.c160077
基金项目: 

国家自然科学基金 61362001

详细信息
    作者简介:

    卢红阳  南昌大学信息工程学院硕士研究生.主要研究方向为稀疏表示在图像处理的应用, 计算机视觉, 遥感数据融合和重建.E-mail:luhongyang6890@163.com

    熊娇娇  南昌大学信息工程学院硕士研究生.主要研究方向为稀疏表示在图像处理的应用, 磁共振图像重建.E-mail:xiongjiaojiao0126@163.com

    王玉皞  博士, 南昌大学信息工程学院教授.主要研究方向为信道建模和无线电测量, 软件无线电, 复杂场景感知监控, 非线性信号处理, 多媒体, 传感网络以及无线通信.E-mail:wangyuhao@ncu.edu.cn

    邓晓华  博士, 南昌大学教授.主要研究方向为空间物理卫星观测和计算机数值研究.E-mail:dengxhua@gmail.com

    通讯作者:

    刘且根  博士, 南昌大学信息工程学院副教授.主要研究方向为稀疏表示在图像处理的应用, 计算机视觉, 磁共振图像重建.E-mail:liuqiegen@ncu.edu.cn

Maximum Weighted Projection Solver for Contrast Preserving Decolorization

Funds: 

National Natural Science Foundation of China 61362001

More Information
    Author Bio:

     Master student at the School of Information Engineering, Nanchang University. Her research interest covers sparse representations theory and its applications in image processing, computer vision, remote sensing data fusion and reconstruction

     Master student at the School of Information Engineering, Nanchang University. Her research interest covers sparse representations theory and its applications in image processing, and magnetic resonance imaging (MRI) reconstruction

     Ph.D., professor at the School of Information Engineering, Nanchang University. His research interest covers channel modeling and radio measurement, software-defined radio, complex environment sensing and monitoring, nonlinear signal processing, multimedia, sensor networks, and wireless communication

     Ph.D., professor at Nanchang University. His research interest covers physical space satellite observations and computer numerical study

    Corresponding author: DENG Xiao-Hua  Ph.D., professor at Nanchang University. His research interest covers physical space satellite observations and computer numerical study
  • 摘要: 针对目前彩色图像灰度化难以充分保留原彩色图像对比度的问题,本文提出了基于最大加权投影求解的彩色图像灰度化模型及算法.首先,在好的彩色图像灰度化算法应使灰度化图像具有最大对比度的假设下,本模型提出最大加权投影的目标优化函数,并且将原始彩色图像梯度权重引入到最大化函数中,使得原彩色图像中对比度较小的区域也能够在灰度化后的图像中得到保持.每个彩色通道梯度的高斯加权系数反映灰度图像的对比度和原彩色图像的颜色顺序.其次,对所提模型使用参数离散搜索策略求解,通过对线性离散参数模型产生的候选图像进行搜索,由于只有几个算术运算,计算速度较快.最后,为评价所提出算法在复杂场景下图像灰度化对比度保持性能,本文对Cadik、CSDD和COLOR250数据集分别进行灰度化实验.定性和定量实验结果表明,所提算法相比于其他算法能较好地保留原彩色图像颜色对比度,同时具有对噪声鲁棒和运算速度快的优势.
  • 图  1  彩色图像灰度化的应用示意图

    Fig.  1  Application diagram of color-to-gray conversion

    图  2  指数变换图

    Fig.  2  Exponential transformation diagram

    图  3  Cadik数据集彩色图像灰度化转化结果

    ((a)输入彩色图像; (b) Gooch算法; (c) rgb2gray算法; (d) CP算法; (e) RTCP算法; (f) MWPDe算法)

    Fig.  3  Color-to-gray conversion results of Cadik dataset

    ((a) Input images; (b) Gooch; (c) rgb2gray; (d) CP; (e) RTCP; (f) MWPDe)

    图  4  CSDD数据集彩色图像灰度化转化结果

    ((a)输入彩色图像; (b) Gooch算法; (c) rgb2gray算法; (d) CP算法; (e) RTCP算法; (f) MWPDe算法)

    Fig.  4  Color-to-gray conversion results of CSDD dataset

    ((a) Input images; (b) Gooch; (c) rgb2gray; (d) CP; \\(e) RTCP; (f) MWPDe)

    图  5  COLOR250数据集彩色图像灰度化转化结果

    ((a)输入彩色图像; (b) Gooch算法; (c) rgb2gray算法; (d) CP算法; (e) RTCP算法; (f) MWPDe算法)

    Fig.  5  Color-to-gray conversion results of COLOR250 dataset

    ((a) Input images; (b) Gooch; (c) rgb2gray; \\(d) CP; (e) RTCP; (f) MWPDe)

    图  6  选择性实验结果

    Fig.  6  Preference experiment result

    图  7  准确性实验结果

    Fig.  7  Accuracy experiment result

    图  8  噪声方差为0.03时对Cadik数据集进行抗噪性能分析

    ((a)输入彩色图像; (b) rgb2gray算法; (c) CP算法; (d) RTCP算法; (e) MWPDe算法)

    Fig.  8  Antinoise performance analysis of Cadik dataset with 0.03 noise variance

    ((a) Input images; (b) rgb2gray; (c) CP; (d) RTCP; (e) MWPDe)

    图  9  算法对低分辨率图像实验效果

    ((a)输入低分辨率彩色图像(下采样2个因子); (b) rgb2gray算法; (c) CP算法; (d) RTCP算法; (e) MWPDe算法)

    Fig.  9  Experiment results on the low resolution images

    ((a) Input low resolution color images (2 downsampling factor); (b) rgb2gray; (c) CP; (d) RTCP; (e) MWPDe)

    图  10  参数敏感度分析

    ((a)相似系数 ${{S}_{x, y}}(t)$ 随 $t$ 的变化趋势; (b)输入彩色图像; (c) $t=0.01$ 时WMPDe算法灰度化效果; (d) $t=0.00001$ 时WMPDe算法灰度化效果)

    Fig.  10  Sensitivity study of parameters

    ((a) Similar coefficients variation tendency ${{S}_{x, y}}(t)$ with $t$ ; (b) Input color images; (c) $t=0.01$ ; (d) $t=0.00001$ )

    表  1  Cadik数据集的CCPR值

    Table  1  CCPR value of Cadik dataset

    τ Gooch rgb2gray CP RTCP MWPDe
    1 0.96 0.93 0.97 0.96 0.96
    2 0.93 0.88 0.94 0.94 0.94
    3 0.91 0.84 0.92 0.92 0.92
    4 0.89 0.8 0.91 0.9 0.91
    5 0.87 0.77 0.89 0.89 0.9
    6 0.85 0.75 0.87 0.87 0.88
    7 0.83 0.73 0.86 0.86 0.87
    8 0.81 0.71 0.84 0.85 0.86
    9 0.79 0.69 0.83 0.83 0.85
    10 0.77 0.68 0.82 0.82 0.84
    11 0.75 0.66 0.8 0.8 0.84
    12 0.72 0.65 0.79 0.79 0.83
    13 0.7 0.63 0.77 0.77 0.82
    14 0.69 0.62 0.76 0.76 0.81
    15 0.67 0.61 0.75 0.75 0.81
    下载: 导出CSV

    表  2  CSDD数据集的CCPR值

    Table  2  CCPR value of CSDD dataset

    τ Gooch rgb2gray CP RTCP MWPDe
    1 0.97 0.96 0.96 0.96 0.95
    2 0.93 0.94 0.93 0.93 0.92
    3 0.91 0.91 0.91 0.91 0.91
    4 0.89 0.89 0.89 0.9 0.89
    5 0.87 0.87 0.87 0.88 0.88
    6 0.85 0.85 0.85 0.87 0.86
    7 0.83 0.83 0.83 0.85 0.85
    8 0.81 0.81 0.81 0.84 0.84
    9 0.79 0.8 0.79 0.82 0.83
    10 0.77 0.78 0.77 0.81 0.82
    11 0.76 0.76 0.75 0.8 0.81
    12 0.74 0.75 0.74 0.78 0.8
    13 0.73 0.73 0.72 0.77 0.79
    14 0.71 0.71 0.7 0.76 0.78
    15 0.7 0.68 0.7 0.75 0.77
    下载: 导出CSV

    表  3  COLOR250数据集的CCPR值

    Table  3  CCPR value of COLOR250

    τ Gooch rgb2gray CP RTCP MWPDe
    1 0.95 0.96 0.96 0.96 0.95
    2 0.92 0.93 0.93 0.93 0.92
    3 0.89 0.9 0.9 0.9 0.9
    4 0.86 0.87 0.88 0.88 0.88
    5 0.83 0.85 0.86 0.86 0.86
    6 0.81 0.83 0.84 0.84 0.85
    7 0.79 0.81 0.82 0.83 0.83
    8 0.76 0.79 0.8 0.81 0.82
    9 0.74 0.77 0.79 0.8 0.81
    10 0.72 0.75 0.77 0.78 0.8
    11 0.7 0.74 0.76 0.77 0.79
    12 0.68 0.72 0.74 0.76 0.78
    13 0.67 0.7 0.73 0.75 0.77
    14 0.65 0.69 0.72 0.74 0.76
    15 0.63 0.67 0.7 0.72 0.75
    下载: 导出CSV

    表  4  噪声情况下Cadik数据集的CCPR值

    Table  4  CCPR value of Cadik dataset with noise added

    τ rgb2gray CP RTCP MWPDe
    1 0.92 0.95 0.94 0.96
    2 0.86 0.91 0.91 0.92
    3 0.82 0.87 0.88 0.89
    4 0.78 0.84 0.85 0.86
    5 0.74 0.82 0.84 0.84
    6 0.72 0.79 0.82 0.82
    7 0.7 0.77 0.81 0.81
    8 0.68 0.75 0.8 0.79
    9 0.66 0.73 0.78 0.78
    10 0.65 0.71 0.77 0.77
    11 0.63 0.7 0.76 0.76
    12 0.62 0.68 0.75 0.75
    13 0.61 0.66 0.74 0.74
    14 0.6 0.65 0.73 0.74
    15 0.58 0.64 0.72 0.73
    下载: 导出CSV

    表  5  输入390×293彩色图像时不同算法的运行时间

    Table  5  Runtime of different algorithms with the input 390×293 color image

    Methods Gooch rgb2gray CP RTCP MWPDe
    Runtime (s) 3.00E+04 0.0048 0.5233 0.0728 0.0343
    下载: 导出CSV
  • [1] Bala R, Eschbach R. Spatial color-to-grayscale transform preserving chrominance edge information. In: Proceedings of the 12th Color Imaging Conference: Color Science and Engineering Systems, Technologies, and Applications. Scottsdale, USA: The Society for Imaging Science and Technology, 2004. 82-86
    [2] Neumann L, Čadík M, Nemcsics A. An efficient perception-based adaptive color to gray transformation. In: Proceedings of the 3rd Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging. Switzerland: Eurographics Association, 2007. 73-80
    [3] Smith K, Landes P-E, Thollot J, Myszkowski K. Apparent greyscale: a simple and fast conversion to perceptually accurate images and video. Computer Graphics Forum, 2008, 27(2): 193-200 doi: 10.1111/j.1467-8659.2008.01116.x
    [4] Gooch A A, Olsen S C, Tumblin J, Gooch B. Color2gray: salience-preserving color removal. ACM Transactions on Graphics, 2005, 24(3): 634-639 doi: 10.1145/1073204
    [5] Rasche K, Geist R, Westall J. Detail preserving reproduction of color images for monochromats and dichromats. IEEE Computer Graphics and Applications, 2005, 25(3): 22-30 doi: 10.1109/MCG.2005.54
    [6] Kim Y, Jang C, Demouth J, Lee S. Robust color-to-gray via nonlinear global mapping. ACM Transactions on Graphics, 2009, 28(5): Article No.161
    [7] Du H, He S F, Sheng B, Ma L Z, Lau R W H. Saliency-guided color-to-gray conversion using region-based optimization. IEEE Transactions on Image Processing, 2015, 24(1): 434-443 doi: 10.1109/TIP.2014.2380172
    [8] Song M L, Tao D P, Chen C, Li X L, Chen C W. Color to gray: visual cue preservation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(9): 1537-1552 doi: 10.1109/TPAMI.2009.74
    [9] Song M L, Tao D P, Chen C, Bu J J, Yang Y Z. Color-to-gray based on chance of happening preservation. Neurocomputing, 2013, 119: 222-231 doi: 10.1016/j.neucom.2013.03.037
    [10] 宋明黎, 王慧琼, 孙陈纯.基于高斯混合模型的色彩转换.计算机辅助设计与图形学学报, 2008, 20(11): 1471-1476 http://www.cnki.com.cn/Article/CJFDTOTAL-JSJF200811014.htm

    Song Ming-Li, Wang Hui-Qiong, Sun Chen-Chun. Gaussian mixture model based approach on color transfer. Journal of Computer-Aided Design and Computer Graphics, 2008, 20(11): 1471-1476 http://www.cnki.com.cn/Article/CJFDTOTAL-JSJF200811014.htm
    [11] 王辉, 陈小雕, 王毅刚.改进的非线性全局映射灰度化方法.计算机辅助设计与图形学学报, 2013, 25(10): 1476-1479 http://www.cnki.com.cn/Article/CJFDTOTAL-JSJF201310007.htm

    Wang Hui, Chen Xiao-Diao, Wang Yi-Gang. The improved color-to-gray via nonlinear global mapping. Journal of Computer-Aided Design and Computer Graphics, 2013, 25(10): 1476-1479 http://www.cnki.com.cn/Article/CJFDTOTAL-JSJF201310007.htm
    [12] 朱薇, 刘利刚. IHLS颜色空间的灰度化全局非线性三向映射.计算机辅助设计与图形学学报, 2013, 25(2): 154-159 http://www.cnki.com.cn/Article/CJFDTOTAL-JSJF201302003.htm

    Zhu Wei, Liu Li-Gang. Decolorize nonlinear tri-mapping in IHLS color space. Journal of Computer-Aided Design and Computer Graphics, 2013, 25(2): 154-159 http://www.cnki.com.cn/Article/CJFDTOTAL-JSJF201302003.htm
    [13] Lu C W, Xu L, Jia J Y. Contrast preserving decolorization. In: Proceedings of the 2012 IEEE International Conference on Computational Photography. Seattle, WA, USA: IEEE, 2012. 1-7
    [14] Lu C W, Xu L, Jia J Y. Real-time contrast preserving decolorization. In: Proceedings of the SIGGRAPH Asia 2012 Technical Briefs. Singapore, Singapore: ACM, 2012. Article No.34
    [15] Lu C W, Xu L, Jia J Y. Contrast preserving decolorization with perception-based quality metrics. International Journal of Computer Vision, 2014, 110(2): 222-239 doi: 10.1007/s11263-014-0732-6
    [16] Song Y B, Bao L C, Yang Q X. Real-time video decolorization using bilateral filtering. In: Proceedings of the 2014 IEEE Winter Conference on Applications of Computer Vision. Steamboat Springs, CO, USA: IEEE, 2014. 159-166
    [17] Liu Q G, Liu X P, Xie W S, Wang Y H, Liang D. GcsDecolor: gradient correlation similarity for efficient contrast preserving decolorization. IEEE Transactions on Image Processing, 2015, 24(9): 2889-2904 doi: 10.1109/TIP.2015.2423615
    [18] Lu G F, Lin Z, Jin Z. Face recognition using regularised generalised discriminant locality preserving projections. IET Computer Vision, 2011, 5(2): 107-116 doi: 10.1049/iet-cvi.2009.0138
    [19] Vasuhi S, Vaidehi V. Identification of human faces using orthogonal locality preserving projections. In: Proceedings of the 2009 International Conference on Signal Processing Systems. Singapore, Singapore: IEEE, 2009. 718-722
    [20] Gu X H, Yang L P, Peng J. Face recognition with discriminant locality preserving projections in complete kernel space. In: Proceedings of the 2011 International Conference on Computer Science and Network Technology. Harbin, China: IEEE, 2011. 1166-1169
    [21] Yi J, Ruan Q Q. Gabor-based improved locality preserving projections for face recognition. In: Proceedings of the 2007 IEEE International Conference on Image Processing. San Antonio, TX, USA: IEEE, 2007. I-153-I-156
    [22] Lin C, Sun X R, Zhou K J, Xia F. Supervised gabor-based kernel locality preserving projections for face recognition. In: Proceedings of the 2011 International Conference on and 4th International Conference on Cyber, Physical and Social Computing Internet of Things (iThings/CPSCom). Dalian, China: IEEE, 2011. 252-257
    [23] Wang H X. A new feature extraction method for image recognition using structural two-dimensional locality preserving projections. In: Proceedings of the 16th IEEE International Conference on Image Processing. Cairo, Egypt: IEEE, 2009. 2037-2040
    [24] Yi J, Ruan Q Q. An image matrix compression based supervised locality preserving projections for face recognition. In: Proceedings of the 2007 International Symposium on Intelligent Signal Processing and Communications Systems. Xiamen, China: IEEE, 2007. 738-741
    [25] He X F, Yan S C, Hu Y X, Niyogi P, Zhang H J. Face recognition using Laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(3): 328-340 doi: 10.1109/TPAMI.2005.55
    [26] Čadík M. Perceptual evaluation of color-to-grayscale image conversions. Computer Graphics Forum, 2008, 27(7): 1745-1754 doi: 10.1111/cgf.2008.27.issue-7
  • 加载中
图(10) / 表(5)
计量
  • 文章访问数:  1863
  • HTML全文浏览量:  430
  • PDF下载量:  483
  • 被引次数: 0
出版历程
  • 收稿日期:  2016-01-22
  • 录用日期:  2016-04-28
  • 刊出日期:  2017-05-01

目录

    /

    返回文章
    返回