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Proceedings Paper

Fully-flexible glass-air disordered fiber imaging through deep learning (Conference Presentation)
Author(s): Sean Pang; Yangyang Sun; Jian Zhao; Axel Schülzgen

Paper Abstract

Computational imaging systems apply encoding on the physical layer of the imaging device, demonstrating superior performance in resolution, dynamic range, and acquisition speed, compared to conventional point-to-point mapping imaging system. However, accurate mathematical models is required for such systems, and the calibration is a major concern for practical implementation. In this invited talk, we will discuss the efforts in applying the learning approach in computational imaging system from the Optical Imaging System Lab at the University of Central Florida. Specifically, the talk will be focus on a demonstration of such approach in fully flexible lensless fiber imaging.

Paper Details

Date Published: 13 May 2019
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Proc. SPIE 10990, Computational Imaging IV, 109900D (13 May 2019); doi: 10.1117/12.2521247
Show Author Affiliations
Sean Pang, CREOL, The College of Optics and Photonics, Univ. of Central Florida (United States)
Yangyang Sun, CREOL, The College of Optics and Photonics, Univ. of Central Florida (United States)
Jian Zhao, CREOL, The College of Optics and Photonics, Univ. of Central Florida (United States)
Axel Schülzgen, CREOL, The College of Optics and Photonics, Univ. of Central Florida (United States)


Published in SPIE Proceedings Vol. 10990:
Computational Imaging IV
Abhijit Mahalanobis; Lei Tian; Jonathan C. Petruccelli, Editor(s)

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