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Quantum-inspired computational imaging.
Altmann, Yoann; McLaughlin, Stephen; Padgett, Miles J; Goyal, Vivek K; Hero, Alfred O; Faccio, Daniele.
Afiliación
  • Altmann Y; School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, UK.
  • McLaughlin S; School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, UK.
  • Padgett MJ; School of Physics and Astronomy, University of Glasgow, Glasgow, UK.
  • Goyal VK; Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA.
  • Hero AO; Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA.
  • Faccio D; School of Physics and Astronomy, University of Glasgow, Glasgow, UK. daniele.faccio@glasgow.ac.uk.
Science ; 361(6403)2018 08 17.
Article en En | MEDLINE | ID: mdl-30115781
Computational imaging combines measurement and computational methods with the aim of forming images even when the measurement conditions are weak, few in number, or highly indirect. The recent surge in quantum-inspired imaging sensors, together with a new wave of algorithms allowing on-chip, scalable and robust data processing, has induced an increase of activity with notable results in the domain of low-light flux imaging and sensing. We provide an overview of the major challenges encountered in low-illumination (e.g., ultrafast) imaging and how these problems have recently been addressed for imaging applications in extreme conditions. These methods provide examples of the future imaging solutions to be developed, for which the best results are expected to arise from an efficient codesign of the sensors and data analysis tools.

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Science Año: 2018 Tipo del documento: Article Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Science Año: 2018 Tipo del documento: Article Pais de publicación: Estados Unidos