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13 May 2019 Fully-flexible glass-air disordered fiber imaging through deep learning (Conference Presentation)
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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.
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Sean Pang, Yangyang Sun, Jian Zhao, and Axel Schülzgen "Fully-flexible glass-air disordered fiber imaging through deep learning (Conference Presentation)", Proc. SPIE 10990, Computational Imaging IV, 109900D (13 May 2019); https://doi.org/10.1117/12.2521247
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