Image processing algorithms for a light-field camera and their application for optical flow diagnostics


  • A.V. Seredkin Novosibirsk State University
  • M.P. Tokarev Kutateladze Institute of Thermophysics of SB RAS



software refocusing algorithm, depth field reconstruction, light-field camera, plenoptic system, optical flow diagnostics, PIV (Particle Image Velocimetry), PTV (Particle Tracking Velocimetry)


Application of modern optoelectronic devices extends research in the field of experimental fluid mechanics. The methods of computational photography gradually penetrate into the various fields of science and technology due to using devices based on these methods. A light-field camera can be used to register a three-dimensional velocity distribution in fluid flows where the location of several panoramic optical sensors is difficult because of restrictions in an optical access and vibrations. in this paper we study the possibility of using a plenoptic system consisting of an industrial light-field camera to diagnose liquid and gas flows. A new software algorithm for computing a depth field of a measurement area is proposed. According to the obtained results, the spatial resolution of the method by depth reaches 1/40th of the depth of the field of the optical system when using 11 MP sensor. This method was used to measure 3D velocity fields of a turbulent jet inside a slot channel throughout its depth. In the future, the number of applications will grow for the cases where the use of plenoptic devices with high spatial resolution is appropriate.

Author Biographies

A.V. Seredkin

M.P. Tokarev


  1. C. Perwass and L. Wietzke, “Single Lens 3D-Camera with Extended Depth-of-Field,” Proc. SPIE 8291, 8-15 (2012).
  2. N. Zeller, F. Quint, and U. Stilla, “Calibration and Accuracy Analysis of a Focused Plenoptic Camera,” in Proc. ISPRS Technical Commission III Symposium, Zürich, Switzerland, September 5-7, 2014 (ISPRS Press, Zürich, 2014), pp. 205-212.
  3. M. P. Tokarev, D. M. Markovich, and A. V. Bilsky, “Adaptive Algorithms for PIV Image Processing,” Vychisl. Tekhnol. 12 (3), 109-131 (2007).
  4. M. V. Shestakov, M. P. Tokarev, and D. M. Markovich, “3D Flow Dynamics in a Turbulent Slot Jet: Time-Resolved Tomographic PIV Measurements,” in Proc. 17th Int. Symp. on Applications of Laser Techniques to Fluid Mechanics, Lisbon, Portugal, July 7-10, 2014 (Inst. Superior Tecnico, Lisbon, 2014), pp. 1-7.
  5. M. V. Shestakov, V. M. Dulin, M. P. Tokarev, et al., “PIV Study of Large-Scale Flow Organisation in Slot Jets,” Int. J. Heat Fluid Fl. 51, 335-352 (2015).
  6. A. V. Bilsky, V. A. Lozhkin, D. M. Markovich, and M. P. Tokarev, “A Maximum Entropy Reconstruction Technique for Tomographic Particle Image Velocimetry,” Meas. Sci. Technol. 24 (4), 1-10 (2013).
    doi 10.1088/0957-0233/24/4/045301
  7. Ye. K. Akhmetbekov, A. V. Bilsky, Yu. A. Lozhkin, et al., “Software for Experiment Management and Processing of Data Obtained by Digital Flow Visualization Techniques (ActualFlow),” Vychisl. Metody Programm. 7, 79-85 (2006).



How to Cite

Серёдкин А., Токарев М. Image Processing Algorithms for a Light-Field Camera and Their Application for Optical Flow Diagnostics // Numerical Methods and Programming (Vychislitel’nye Metody i Programmirovanie). 2016. 17. 224-233. doi 10.26089/NumMet.v17r321



Section 1. Numerical methods and applications