Efficient Reconstruction of Holographic Lens-Free Images by Sparse Phase Recovery

  • Benjamin D. Haeffele
  • Richard Stahl
  • Geert Vanmeerbeeck
  • René Vidal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10434)

Abstract

Digital holographic lens-free imaging is based on recording the diffraction pattern of light after it passes through a specimen and post-processing the recorded diffraction pattern to reconstruct an image of the specimen. If the full, complex-valued wave-front of the diffraction pattern could be recorded then the image reconstruction process would be straight-forward, but unfortunately image sensors typically only record the amplitude of the diffraction pattern but not the phase. As a result, many conventional reconstruction techniques suffer from substantial artifacts and degraded image quality. This paper presents a computationally efficient technique to reconstruct holographic lens-free images based on sparsity, which improves image quality over existing techniques, allows for the possibility of reconstructing images over a 3D volume of focal-depths simultaneously from a single recorded hologram, provides a robust estimate of the missing phase information in the hologram, and automatically identifies the focal depths of the imaged objects in a robust manner.

Keywords

Holography Lens-free imaging Sparsity 

Notes

Acknowledgments

The authors thank Florence Yellin, Lin Zhou, Sophie Roth, Murali Jayapala, Christian Pick, and Stuart Ray for insightful discussions. This work was funded by miDIAGNOSTICS.

Supplementary material

451304_1_En_13_MOESM1_ESM.pdf (178 kb)
Supplementary material 1 (pdf 177 KB)

References

  1. 1.
    Denis, L., Lorenz, D., Thiébaut, E., Fournier, C., Trede, D.: Inline hologram reconstruction with sparsity constraints. Opt. Lett. 34(22), 3475–3477 (2009)CrossRefGoogle Scholar
  2. 2.
    Denis, L., Fournier, C., Fournel, T., Ducottet, C.: Twin-image noise reduction by phase retrieval in in-line digital holography. In: Optics & Photonics 2005, p. 59140J. International Society for Optics and Photonics (2005)Google Scholar
  3. 3.
    Hennelly, B.M., Kelly, D.P., Pandey, N., Monaghan, D.S.: Review of twin reduction and twin removal techniques in holography. National University of Ireland Maynooth (2009)Google Scholar
  4. 4.
    Kim, M.K.: Digital Holographic Microscopy. Springer, New York (2011)CrossRefGoogle Scholar
  5. 5.
    Rivenson, Y., et al.: Sparsity-based multi-height phase recovery in holographic microscopy. Sci. Rep. 6, 37862 (2016). doi: 10.1038/srep37862 CrossRefGoogle Scholar
  6. 6.
    Song, J., et al.: Sparsity-based pixel super resolution for lens-free digital in-line holography. Sci. Rep. 6, 24681 (2016). doi: 10.1038/srep24681 CrossRefGoogle Scholar
  7. 7.
    Xu, Y., Yin, W.: A block coordinate descent method for regularized multiconvex optimization with applications to nonnegative tensor factorization and completion. SIAM J. Imaging Sci. 6(3), 1758–1789 (2013)CrossRefMATHMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Benjamin D. Haeffele
    • 1
  • Richard Stahl
    • 2
  • Geert Vanmeerbeeck
    • 2
  • René Vidal
    • 1
  1. 1.Center for Imaging ScienceJohns Hopkins UniversityBaltimoreUSA
  2. 2.ImecLeuvenBelgium

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