Learning optimal parameters for binary sensing image reconstruction algorithms

Renan A. Rojas, Wangyu Luo, Victor Murray, Yue M. Lu

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

10 Citas (Scopus)

Resumen

A novel data-driven reconstruction algorithm for quantum image sensors is proposed. Binary observations are efficiently decoded by modeling the reconstruction structure as a two-layer neural network, where optimal coefficients are obtained via error backpropagation. Such a model encapsulates the structure of state-of-the-art algorithms, yet it presents a considerably faster alternative which adapts to input examples without a priori statistical information. Simulations on natural and synthetic datasets show accurate reconstructions with structural similarities consistent with the state of the art, while requiring approximately 5 times less computational cost.

Idioma originalInglés
Título de la publicación alojada2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
EditorialIEEE Computer Society
Páginas2791-2795
Número de páginas5
ISBN (versión digital)9781509021758
DOI
EstadoPublicada - 2 jul. 2017
Publicado de forma externa
Evento24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China
Duración: 17 set. 201720 set. 2017

Serie de la publicación

NombreProceedings - International Conference on Image Processing, ICIP
Volumen2017-September
ISSN (versión impresa)1522-4880

Conferencia

Conferencia24th IEEE International Conference on Image Processing, ICIP 2017
País/TerritorioChina
CiudadBeijing
Período17/09/1720/09/17

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