A compressed sensing approach to interpolation of fractional Brownian trajectories for a single particle tracking experiment

被引:0
作者
Muszkieta, Monika [1 ]
Janczura, Joanna [1 ]
机构
[1] Wroclaw Univ Sci & Technol, Fac Pure & Appl Math, Hugo Steinhaus Ctr, Wyb Wyspanskiego 27, PL-50370 Wroclaw, Poland
关键词
The single particle tracking; Trajectory interpolation; Fractional Brownian motion; Missing data; Compressed sensing; ROBUST UNCERTAINTY PRINCIPLES; THRESHOLDING ALGORITHM; ANOMALOUS DIFFUSION; LATERAL DIFFUSION; SIGNAL RECOVERY; ERRORS;
D O I
10.1016/j.amc.2023.127900
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
In this paper, assuming that particles undergo the fractional Brownian motion, we pro-pose the interpolation model based on the fact that the spectral density derived for the finite-length realization of this process obeys a power law decay. This allows us to apply the main idea of compressed sensing to reconstruct a given trajectory in the frequency domain. We conduct a simulation study with various trajectory degradation models re-flecting typical limitations found in a single particle tracking experiment. Based on the statistical analysis we show that parameters characterizing the fractional Brownian motion estimated from trajectories interpolated by the proposed method are close to the ones es-timated from the ground truth data.(c) 2023 Elsevier Inc. All rights reserved.
引用
收藏
页数:12
相关论文
共 35 条
  • [1] [Anonymous], 1999, Convex analysis and variational problems
  • [2] A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
    Beck, Amir
    Teboulle, Marc
    [J]. SIAM JOURNAL ON IMAGING SCIENCES, 2009, 2 (01): : 183 - 202
  • [3] Measurement of anomalous diffusion using recurrent neural networks
    Bo, Stefano
    Schmidt, Falko
    Eichhorn, Ralf
    Volpe, Giovanni
    [J]. PHYSICAL REVIEW E, 2019, 100 (01)
  • [4] Single-pixel interior filling function approach for detecting and correcting errors in particle tracking
    Burov, Stanislav
    Figliozzi, Patrick
    Lin, Binhua
    Rice, Stuart A.
    Scherer, Norbert F.
    Dinner, Aaron R.
    [J]. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2017, 114 (02) : 221 - 226
  • [5] Robust uncertainty principles:: Exact signal reconstruction from highly incomplete frequency information
    Candès, EJ
    Romberg, J
    Tao, T
    [J]. IEEE TRANSACTIONS ON INFORMATION THEORY, 2006, 52 (02) : 489 - 509
  • [6] Quantitative robust uncertainty principles and optimally sparse decompositions
    Candès, Emmanuel J.
    Romberg, Justin
    [J]. FOUNDATIONS OF COMPUTATIONAL MATHEMATICS, 2006, 6 (02) : 227 - 254
  • [7] Near-optimal signal recovery from random projections: Universal encoding strategies?
    Candes, Emmanuel J.
    Tao, Terence
    [J]. IEEE TRANSACTIONS ON INFORMATION THEORY, 2006, 52 (12) : 5406 - 5425
  • [8] Stable signal recovery from incomplete and inaccurate measurements
    Candes, Emmanuel J.
    Romberg, Justin K.
    Tao, Terence
    [J]. COMMUNICATIONS ON PURE AND APPLIED MATHEMATICS, 2006, 59 (08) : 1207 - 1223
  • [9] Chambolle A, 2004, J MATH IMAGING VIS, V20, P89
  • [10] Objective comparison of particle tracking methods
    Chenouard, Nicolas
    Smal, Ihor
    de Chaumont, Fabrice
    Maska, Martin
    Sbalzarini, Ivo F.
    Gong, Yuanhao
    Cardinale, Janick
    Carthel, Craig
    Coraluppi, Stefano
    Winter, Mark
    Cohen, Andrew R.
    Godinez, William J.
    Rohr, Karl
    Kalaidzidis, Yannis
    Liang, Liang
    Duncan, James
    Shen, Hongying
    Xu, Yingke
    Magnusson, Klas E. G.
    Jalden, Joakim
    Blau, Helen M.
    Paul-Gilloteaux, Perrine
    Roudot, Philippe
    Kervrann, Charles
    Waharte, Francois
    Tinevez, Jean-Yves
    Shorte, Spencer L.
    Willemse, Joost
    Celler, Katherine
    van Wezel, Gilles P.
    Dan, Han-Wei
    Tsai, Yuh-Show
    Ortiz de Solorzano, Carlos
    Olivo-Marin, Jean-Christophe
    Meijering, Erik
    [J]. NATURE METHODS, 2014, 11 (03) : 281 - U247