Research on Spatially Adaptive High-Order Total Variation Model for Weak Fluorescence Image Restoration

被引:0
|
作者
马进 [1 ]
薛腾 [1 ]
邵全全 [1 ]
胡洁 [1 ]
王伟明 [1 ]
机构
[1] State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University
基金
中国国家自然科学基金;
关键词
confocal microscopy; weak fluorescence; image restoration; spatially adaptive high-order total variation(SA-HOTV);
D O I
暂无
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Confocal laser scanning microscopy(CLSM) has emerged as one of the most advanced fluorescence cell imaging techniques in the field of biomedicine. However, fluorescence cell imaging is limited by spatial blur and additive white noise induced by the excitation light. In this paper, a spatially adaptive high-order total variation(SA-HOTV) model for weak fluorescence image restoration is proposed to conduct image restoration. The method consists of two steps: optimizing the deconvolution model of the fluorescence image by the generalized Lagrange equation and alternating direction method of multipliers(ADMM); using spatially adaptive parameters to balance the image fidelity and the staircase effect. Finally, an comparison of SA-HOTV model and Richardson-Lucy model with total variation(RL-TV model) indicates that the proposed method can preserve the image details ultimately,reduce the staircase effect substantially and further upgrade the quality of the restored weak fluorescence image.
引用
收藏
页码:1 / 7
页数:7
相关论文
共 50 条
  • [41] High-order total variation minimization for interior tomography
    Yang, Jiansheng
    Yu, Hengyong
    Jiang, Ming
    Wang, Ge
    [J]. INVERSE PROBLEMS, 2010, 26 (03)
  • [42] High-order total variation minimization for interior SPECT
    Yang, Jiansheng
    Yu, Hengyong
    Jiang, Ming
    Wang, Ge
    [J]. INVERSE PROBLEMS, 2012, 28 (01)
  • [43] Convergence bound in total variation for an image restoration model
    Jovanovski, Oliver
    [J]. STATISTICS & PROBABILITY LETTERS, 2014, 90 : 11 - 16
  • [44] Primal-dual splitting method for high-order model with application to image restoration
    Mei, Jin-Jin
    Huang, Ting-Zhu
    [J]. APPLIED MATHEMATICAL MODELLING, 2016, 40 (03) : 2322 - 2332
  • [45] Image Deblurring with Adaptive Total Variation Model
    Bai, Yang
    Ding, Yuanyuan
    Zhang, Xin
    Jia, Hongguang
    Guo, Lihong
    [J]. INTERNATIONAL CONFERENCE ON IMAGE PROCESSING AND PATTERN RECOGNITION IN INDUSTRIAL ENGINEERING, 2010, 7820
  • [46] Image Denoising Via Spatially Adaptive Directional Total Generalized Variation
    Tavakkol, Elaheh
    Dong, Yiqiu
    Hosseini, Seyed-Mohammad
    [J]. IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY TRANSACTION A-SCIENCE, 2022, 46 (04): : 1283 - 1294
  • [47] Image Denoising Via Spatially Adaptive Directional Total Generalized Variation
    Elaheh Tavakkol
    Yiqiu Dong
    Seyed-Mohammad Hosseini
    [J]. Iranian Journal of Science and Technology, Transactions A: Science, 2022, 46 : 1283 - 1294
  • [48] WEIGHTED-TYPE IMAGE SEGMENTATION MODEL VIA COUPLING HEAT KERNEL CONVOLUTION WITH HIGH-ORDER TOTAL VARIATION
    Geng, Mengxiao
    Yang, Lin
    Pang, Zhi-Feng
    Zhu, Haohui
    [J]. JOURNAL OF NONLINEAR AND VARIATIONAL ANALYSIS, 2023, 7 (04): : 487 - 503
  • [49] Novel image restoration model coupling gradient fidelity term based on adaptive total variation
    Shi, Ming-Zhu
    Xu, Ting-Fa
    Liang, Jiong
    Feng, Liang
    Zhang, Kun
    Zhou, Li-Wei
    [J]. Journal of Beijing Institute of Technology (English Edition), 2011, 20 (02): : 261 - 266
  • [50] Novel image restoration model coupling gradient fidelity term based on adaptive total variation
    石明珠
    许廷发
    梁炯
    冯亮
    张坤
    周立伟
    [J]. Journal of Beijing Institute of Technology, 2011, 20 (02) : 261 - 266