结合松弛中值滤波的高阶彩色图像迭代去噪算法

芦碧波, 李阳, 王永茂

芦碧波, 李阳, 王永茂. 结合松弛中值滤波的高阶彩色图像迭代去噪算法[J]. 应用光学, 2016, 37(3): 365-371. DOI: 10.5768/JAO201637.0302001
引用本文: 芦碧波, 李阳, 王永茂. 结合松弛中值滤波的高阶彩色图像迭代去噪算法[J]. 应用光学, 2016, 37(3): 365-371. DOI: 10.5768/JAO201637.0302001
Lu Bibo, Li Yang, Wang Yongmao. Color image denoising using high order iterating model by combining relaxed median filter[J]. Journal of Applied Optics, 2016, 37(3): 365-371. DOI: 10.5768/JAO201637.0302001
Citation: Lu Bibo, Li Yang, Wang Yongmao. Color image denoising using high order iterating model by combining relaxed median filter[J]. Journal of Applied Optics, 2016, 37(3): 365-371. DOI: 10.5768/JAO201637.0302001

结合松弛中值滤波的高阶彩色图像迭代去噪算法

基金项目: 

国家自然科学基金委河南人才联合培养基金 (U1404103); 国家留学基金委河南省地方合作项目(2013(5045)) ;河南省教育厅科学技术研究重点项目(14A520029,15A520070);河南理工大学创新型科研团队项目(T2014-3)

详细信息
    作者简介:

    芦碧波(1978-),男,河南焦作人,博士,副教授,主要研究方向有图像去噪,图像分割,图像融合,色调映射等。 E-mail:lubibojz@gmail.com

  • 中图分类号: TN911.73

Color image denoising using high order iterating model by combining relaxed median filter

  • 摘要: 为去除基于局部平均曲率的彩色图像去噪模型中作为几何特征而保留下来的斑点,提出了一种改进的迭代算法。采用局部平均曲率作为正则项耦合各个颜色通道,在迭代过程中根据局部统计量检测斑点,并引入松弛中值滤波进行斑点抑制。使用不同特征的图像进行仿真实验,并对峰值信噪比的演化进行分析。实验结果表明,改进的算法在有效消除斑点的同时较好地保护了图像结构,并且提高了计算效率。峰值信噪比提高了2.47%,迭代次数减少了93.66%。
    Abstract: An improved iterating algorithm was proposed to eliminate the speckles preserved by the local curvaturebased model as geometrical characteristic. It utilized the local curvature coupling 3 channels as the regularizing term,then detected speckles by using local statistics values. The relaxed median filter was introduced to suppress these speckles. Numerical experiments using images of different features were carried out and the evolution of the values of the peak signal to noise ratio(PSNR) was analyzed. The results show that this algorithm can accelerate the progress of evolution and eliminate the speckles while protecting the image structure information. The value of the PSNR increases by 2.47%, and the iterations decrease by 93.66%.
  • [1]Rudin L I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms[J]. Physica Dnonlinear Phenomena, 1992, 60(14):259268.
    [2]Lysaker M, Lundervold A, Tai X C. Noise removal using fourthorder partial differential equation with applications to medical magnetic resonance images in space and time[J]. IEEE Transactions on Image Processing, 2003, 12(12):1579  1590.
    [3]Bredies K, Kunisch K, Pock T. Total generalized variation[J]. Siam Journal on Imaging Sciences, 2010, 3(3):492526.
    [4]Zhu W, Chan T. Image denoising using mean curvature of image surface[J]. Siam Journal on Imaging Sciences, 2012, 5(1):132.
    [5]Blomgren P , Chan T F. Color TV: total variation methods for restoration of vectorvalued images[J]. IEEE Transactions on Image Processing, 1998, 7(3):304309.
    [6]Bresson X, Chan T F. Fast dual minimization of the vectorial total variation norm and applications to color image processing[J]. Inverse Problems & Imaging, 2008, 2(4):455484.
    [7]Miyata T, Sakai Y. Vectorized total variation defined by weighted L infinity norm for utilizing inter channel dependency[C]// Image Processing (ICIP), 2012 19th IEEE International Conference on IEEE, Sept.30Oct.3,2012,Orlando, FL.USA:IEEE,c2012:30573060.
    [8]Ono S, Yamada I. A convex regularizer for reducing color artifact in color image recovery[C]// Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on IEEE, June 2328,2013,Portland, OR.USA:IEEE,c2013:17751781.
    [9]Bredies K. Recovering piecewise smooth multichannel images by minimization of convex functionals with total generalized variation penalty[J]. Lecture Notes in Computer Science, 2014,8293:4477.
    [10]Miyata T. L infinity total generalized variation for color image recovery[C]// Image Processing (ICIP), 2013 20th IEEE International Conference on IEEE, Sept.1518, Melbourne, VIC.USA:IEEE,c2013:449453.
    [11]Carlos B L, Ke C. On highorder denoising models and fast algorithms for vectorvalued images[J]. IEEE Transactions on Image Processing, 2010, 19(6):15181527.
    [12]Sun Li. A splitting mean curvaturebased model for color image denoising[J]. Journal of Lanzhou University(Natural Sciences), 2012,48(3):128132.
    孙莉. 一种基于平均曲率的彩色图像去噪分裂模型[J]. 兰州大学学报:自然科学版, 2012, 48(3):128132.
    [13]Hamza A B, Luque E P L, Martínez A J, et al. Removing noise and preserving details with relaxed median filters[J]. Journal of Mathematical Imaging & Vision, 1999, 11(2):161177.
    [14]Chang Liangliang, Wang Guanglong. Denoising method for mixed noise based on median filter and lifting wavelet transform[J]. Journal of Applied Optics, 2012, 33(5):894898.
    常亮亮, 王广龙. 基于中值滤波和提升小波分析的图像去噪方法研究[J]. 应用光学, 2012, 33(5):894898.
    [15]Li Jinlun, Cui Shaohui, Wang Ming. Threshold denoising method for mixed noise based on improved median filter and lifting wavelet transform[J]. Journal of Applied Optics, 2014, 35(5):817822.
    李金伦, 崔少辉, 汪明. 基于改进中值滤波和提升小波变换的阈值去噪方法研究[J]. 应用光学, 2014, 35(5):817822.
    [16]You Y L, Kaveh M . Fourthorder partial differential equations for noise removal.[J]. IEEE Transactions on Image Processing, 2000, 9(10):17231730.
    [17]Rajan J, Kannan K, Kaimal M R. An improved hybrid model for molecular image denoising[J]. Journal of Mathematical Imaging & Vision, 2008, 31(1):7379.
  • 期刊类型引用(1)

    1. 刘尚旺, 郜刘阳, 王博. 联合双边滤波器和小波阈值收缩去噪算法研究. 国土资源遥感. 2018(02): 114-124 . 百度学术

    其他类型引用(1)

计量
  • 文章访问数:  1324
  • HTML全文浏览量:  62
  • PDF下载量:  45
  • 被引次数: 2
出版历程
  • 收稿日期:  2015-12-03
  • 修回日期:  2016-02-29
  • 刊出日期:  2016-05-14

目录

    /

    返回文章
    返回