Enhanced learning-based imaging with thin-film luminescent concentrators

Alexander Koppelhuber; S. Fanello; Clemens Birklbauer; David Schedl; Shahram Izadi; Oliver Bimber

doi:10.1364/OE.22.029531

Enhanced learning-based imaging with thin-film luminescent concentrators

Alexander Koppelhuber, S. Fanello, Clemens Birklbauer, David Schedl, Shahram Izadi, Oliver Bimber

Institute of Computer Graphics

Research output: Contribution to journal › Article › peer-review

Abstract

LumiConSense, a transparent, flexible, scalable, and disposable thin-film image sensor has the potential to lead to new human-computer interfaces that are unconstrained in shape and sensing-distance. In this article we make four new contributions: (1) A new real-time image reconstruction method that results in a significant enhancement of image quality compared to previous approaches; (2) the efficient combination of image reconstruction and shift-invariant linear image processing operations; (3) various hardware and software prototypes which, realize the above contributions, demonstrating the current potential of our sensor for real-time applications; and finally, (4) a further higher quality offline reconstruction algorithm.

Original language	English
Pages (from-to)	29531-29543
Number of pages	13
Journal	Optics Express
Volume	22
Issue number	24
DOIs	https://doi.org/10.1364/OE.22.029531
Publication status	Published - Nov 2014

Fields of science

102 Computer Sciences
102003 Image processing
102008 Computer graphics
102015 Information systems
102020 Medical informatics
103021 Optics

JKU Focus areas

Engineering and Natural Sciences (in general)

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10.1364/OE.22.029531

Cite this

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title = "Enhanced learning-based imaging with thin-film luminescent concentrators",

abstract = "LumiConSense, a transparent, flexible, scalable, and disposable thin-film image sensor has the potential to lead to new human-computer interfaces that are unconstrained in shape and sensing-distance. In this article we make four new contributions: (1) A new real-time image reconstruction method that results in a significant enhancement of image quality compared to previous approaches; (2) the efficient combination of image reconstruction and shift-invariant linear image processing operations; (3) various hardware and software prototypes which, realize the above contributions, demonstrating the current potential of our sensor for real-time applications; and finally, (4) a further higher quality offline reconstruction algorithm.",

author = "Alexander Koppelhuber and S. Fanello and Clemens Birklbauer and David Schedl and Shahram Izadi and Oliver Bimber",

year = "2014",

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language = "English",

volume = "22",

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AU - Koppelhuber, Alexander

AU - Fanello, S.

AU - Birklbauer, Clemens

AU - Schedl, David

AU - Izadi, Shahram

AU - Bimber, Oliver

PY - 2014/11

Y1 - 2014/11

N2 - LumiConSense, a transparent, flexible, scalable, and disposable thin-film image sensor has the potential to lead to new human-computer interfaces that are unconstrained in shape and sensing-distance. In this article we make four new contributions: (1) A new real-time image reconstruction method that results in a significant enhancement of image quality compared to previous approaches; (2) the efficient combination of image reconstruction and shift-invariant linear image processing operations; (3) various hardware and software prototypes which, realize the above contributions, demonstrating the current potential of our sensor for real-time applications; and finally, (4) a further higher quality offline reconstruction algorithm.

AB - LumiConSense, a transparent, flexible, scalable, and disposable thin-film image sensor has the potential to lead to new human-computer interfaces that are unconstrained in shape and sensing-distance. In this article we make four new contributions: (1) A new real-time image reconstruction method that results in a significant enhancement of image quality compared to previous approaches; (2) the efficient combination of image reconstruction and shift-invariant linear image processing operations; (3) various hardware and software prototypes which, realize the above contributions, demonstrating the current potential of our sensor for real-time applications; and finally, (4) a further higher quality offline reconstruction algorithm.

UR - http://www.opticsinfobase.org/oe/abstract.cfm?uri=oe-22-24-29531

U2 - 10.1364/OE.22.029531

DO - 10.1364/OE.22.029531

M3 - Article

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VL - 22

SP - 29531

EP - 29543

JO - Optics Express

JF - Optics Express

IS - 24

ER -

Enhanced learning-based imaging with thin-film luminescent concentrators

Abstract

Fields of science

JKU Focus areas

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