Presentation
13 May 2019 Fully-flexible glass-air disordered fiber imaging through deep learning (Conference Presentation)
Author Affiliations +
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.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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
Advertisement
Advertisement
KEYWORDS
Image quality

Image transmission

Imaging systems

Endoscopy

Image restoration

Neural networks

Optical components

Back to Top