IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v7y2016i1d10.1038_ncomms13359.html
   My bibliography  Save this article

Exploiting the speckle-correlation scattering matrix for a compact reference-free holographic image sensor

Author

Listed:
  • KyeoReh Lee

    (Korea Advanced Institute of Science and Technology)

  • YongKeun Park

    (Korea Advanced Institute of Science and Technology)

Abstract

The word ‘holography’ means a drawing that contains all of the information for light—both amplitude and wavefront. However, because of the insufficient bandwidth of current electronics, the direct measurement of the wavefront of light has not yet been achieved. Though reference-field-assisted interferometric methods have been utilized in numerous applications, introducing a reference field raises several fundamental and practical issues. Here we demonstrate a reference-free holographic image sensor. To achieve this, we propose a speckle-correlation scattering matrix approach; light-field information passing through a thin disordered layer is recorded and retrieved from a single-shot recording of speckle intensity patterns. Self-interference via diffusive scattering enables access to impinging light-field information, when light transport in the diffusive layer is precisely calibrated. As a proof-of-concept, we demonstrate direct holographic measurements of three-dimensional optical fields using a compact device consisting of a regular image sensor and a diffusor.

Suggested Citation

  • KyeoReh Lee & YongKeun Park, 2016. "Exploiting the speckle-correlation scattering matrix for a compact reference-free holographic image sensor," Nature Communications, Nature, vol. 7(1), pages 1-7, December.
  • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms13359
    DOI: 10.1038/ncomms13359
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/ncomms13359
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/ncomms13359?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tuqiang Pan & Jianwei Ye & Haotian Liu & Fan Zhang & Pengbai Xu & Ou Xu & Yi Xu & Yuwen Qin, 2024. "Non-orthogonal optical multiplexing empowered by deep learning," Nature Communications, Nature, vol. 15(1), pages 1-8, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms13359. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.