Structure-preserving noise reduction in biological Imaging

被引:0
|
作者
Fernandez, J. J. [1 ,2 ]
Li, S. [1 ]
Lucic, V. [3 ]
机构
[1] MRC, Mol Biol Lab, Hills Rd, Cambridge CB2 2QH, England
[2] Univ Almera, Dept Comp Architecture, Almera, *, Spain
[3] Max Planck Inst Biochem, Dept Biol Struct, Martinsried, Germany
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
An approach for noise filtering based on anisotropic nonlinear diffusion is presented. The method combines edge-preserving noise reduction with a strategy to enhance local structures and a mechanism to further smooth the background. The performance is illustrated with its application to electron cryotomography, a leading imaging technique for visualizing the molecular architecture of complex biological specimens. A challenging task in this discipline is to increase the extremely low signal-to-noise ratio to allow visualization and interpretation of the three-dimensional structures. The filtering method presented here succeeds in substantially reducing the noise with excellent preservation of the structures.
引用
收藏
页码:385 / +
页数:2
相关论文
共 50 条
  • [41] Sparse-Representation-Based Classification with Structure-Preserving Dimension Reduction
    Xu, Jin
    Yang, Guang
    Yin, Yafeng
    Man, Hong
    He, Haibo
    COGNITIVE COMPUTATION, 2014, 6 (03) : 608 - 621
  • [42] Structure-preserving style transfer
    Calvo, Santiago
    Serrano, Ana
    Gutierrez, Diego
    Masia, Belen
    XXIX SPANISH COMPUTER GRAPHICS CONFERENCE (CEIG19), 2019, : 25 - 30
  • [43] Structure-Preserving Hierarchical Decompositions
    Irene Finocchi
    Rossella Petreschi
    Theory of Computing Systems, 2005, 38 : 687 - 700
  • [44] Structure-Preserving Instance Generation
    Malitsky, Yuri
    Merschformann, Marius
    O'Sullivan, Barry
    Tierney, Kevin
    LEARNING AND INTELLIGENT OPTIMIZATION (LION 10), 2016, 10079 : 123 - 140
  • [45] Structure-preserving model reduction for nonlinear port-Hamiltonian systems
    Beattie, Christopher
    Gugercin, Serkan
    2011 50TH IEEE CONFERENCE ON DECISION AND CONTROL AND EUROPEAN CONTROL CONFERENCE (CDC-ECC), 2011, : 6564 - 6569
  • [46] Structure-preserving model reduction of passive and quasi-active neurons
    Hedrick, Kathryn R.
    Cox, Steven J.
    JOURNAL OF COMPUTATIONAL NEUROSCIENCE, 2013, 34 (01) : 1 - 26
  • [47] Sparse-Representation-Based Classification with Structure-Preserving Dimension Reduction
    Jin Xu
    Guang Yang
    Yafeng Yin
    Hong Man
    Haibo He
    Cognitive Computation, 2014, 6 : 608 - 621
  • [48] Structure-preserving model reduction using a Krylov subspace projection formulation
    Li, RC
    Bai, ZJ
    COMMUNICATIONS IN MATHEMATICAL SCIENCES, 2005, 3 (02) : 179 - 199
  • [49] Structure-preserving model reduction of passive and quasi-active neurons
    Kathryn R. Hedrick
    Steven J. Cox
    Journal of Computational Neuroscience, 2013, 34 : 1 - 26
  • [50] Structure-preserving deep learning
    Celledoni, E.
    Ehrhardt, M. J.
    Etmann, C.
    Mclachlan, R., I
    Owren, B.
    Schonlieb, C-B
    Sherry, F.
    EUROPEAN JOURNAL OF APPLIED MATHEMATICS, 2021, 32 (05) : 888 - 936