This paper examines the application of a deep learning approach to converting night-time images to day-time images. In particular, we show that a convolutional neural network enables the simulation of artificial and ambient light on images. In this paper, we illustrate the design of the deep neural network and some preliminary results on a real indoor environment and two virtual environments rendered with a 3D graphics engine. The experimental results are encouraging and confirm that a convolutional neural network is an interesting approach in the fields of photo-editing and digital image postprocessing.

Converting night-time images to day-time images through a deep learning approach

Capece, Nicola;Erra, Ugo;
2017

Abstract

This paper examines the application of a deep learning approach to converting night-time images to day-time images. In particular, we show that a convolutional neural network enables the simulation of artificial and ambient light on images. In this paper, we illustrate the design of the deep neural network and some preliminary results on a real indoor environment and two virtual environments rendered with a 3D graphics engine. The experimental results are encouraging and confirm that a convolutional neural network is an interesting approach in the fields of photo-editing and digital image postprocessing.
9781538608319
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11563/132260
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 5
  • ???jsp.display-item.citation.isi??? 3
social impact