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HDR-LFNet: Inverse tone mapping using fusion network

Authors :
Mathieu Chambe
Ewa Kijak
Zoltan Miklos
Olivier Le Meur
Rémi Cozot
Kadi Bouatouch
Declarative & Reliable management of Uncertain, user-generated Interlinked Data (DRUID)
GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7)
Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA)
Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes)
Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique)
Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes)
Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA)
Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)
Creating and exploiting explicit links between multimedia fragments (LinkMedia)
Inria Rennes – Bretagne Atlantique
Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAL, IMAGE ET LANGAGE (IRISA-D6)
Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique)
Computational Visual Perception and Applications ( PERCEPT)
SIGNAL, IMAGE ET LANGAGE (IRISA-D6)
Université du Littoral Côte d'Opale (ULCO)
Laboratoire d'Informatique Signal et Image de la Côte d'Opale (LISIC)
InterDigital R&D France
Source :
Computers and Graphics, Computers and Graphics, 2023, 114, pp.1-12. ⟨10.1016/j.cag.2023.05.007⟩
Publication Year :
2023
Publisher :
Elsevier BV, 2023.

Abstract

International audience; To capture the real-world luminance values, High Dynamic Range (HDR) image processing has been developed. HDR images have a richer content than the widely-used Standard Dynamic Range (SDR) images, and are used in a number of situations, e.g. in film industry. As HDR displays are more and more commercially available, we need to be able to process HDR images as well as SDR ones (for example, devising denoising algorithms, inpainting or anti-aliasing). The most powerful methods to process images are now deep neural networks. However, the training of such networks calls for a lot of images, and HDR images datasets are relatively small.One way to generate HDR images is inverse tone mapping operators (iTMOs). They are algorithms that expand the dynamic range of SDR images. In this paper, we propose HDR-LFNet, a novel iTMO, and its HDR training dataset. Our method merges several existing handcrafted iTMOs, combined in a supervised neural network to produce an HDR output. Our lightweight network requires less training images than state-of-the-art methods, and has faster training phase. Besides, the quality of the generated images is similar to the state-of-the-art. We present the architecture as well as the subjective and experimental evaluations of our method.

Details

ISSN :
00978493
Database :
OpenAIRE
Journal :
Computers & Graphics
Accession number :
edsair.doi.dedup.....acbba4e481749c3fcdcc303f60d7c52c