Please use this identifier to cite or link to this item: http://hdl.handle.net/2289/6478
Title: Real-time imaging through strongly scattering media: seeing through turbid media, instantly
Authors: Sudarsanam, Sriram
Mathew, James
Panigrahi, Swapnesh
Fade, Julien
Alouini, Mehdi
Ramachandran, Hema
Issue Date: Apr-2016
Publisher: Nature Publishing Group
Citation: Scientifc Reports, 2016, Vol. 6, 25033
Abstract: Numerous everyday situations like navigation, medical imaging and rescue operations require viewing through optically inhomogeneous media. This is a challenging task as photons propagate predominantly diffusively (rather than ballistically) due to random multiple scattering off the inhomogenieties. Real-time imaging with ballistic light under continuous-wave illumination is even more challenging due to the extremely weak signal, necessitating voluminous data-processing. Here we report imaging through strongly scattering media in real-time and at rates several times the critical flicker frequency of the eye, so that motion is perceived as continuous. Two factors contributed to the speedup of more than three orders of magnitude over conventional techniques - the use of a simplified algorithm enabling processing of data on the fly, and the utilisation of task and data parallelization capabilities of typical desktop computers. The extreme simplicity of the technique, and its implementation with present day low-cost technology promises its utility in a variety of devices in maritime, aerospace, rail and road transport, in medical imaging and defence. It is of equal interest to the common man and adventure sportsperson like hikers, divers, mountaineers, who frequently encounter situations requiring realtime imaging through obscuring media. As a specific example, navigation under poor visibility is examined.
Description: Open Access
URI: http://hdl.handle.net/2289/6478
ISSN: 2045-2322 (online)
Alternative Location: http://dx.doi.org/10.1038/srep25033
Copyright: 2016 Nature Publishing Group
Appears in Collections:Research Papers (LAMP)

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