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      Explorable Tone Mapping Operators

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          Abstract

          Tone-mapping plays an essential role in high dynamic range (HDR) imaging. It aims to preserve visual information of HDR images in a medium with a limited dynamic range. Although many works have been proposed to provide tone-mapped results from HDR images, most of them can only perform tone-mapping in a single pre-designed way. However, the subjectivity of tone-mapping quality varies from person to person, and the preference of tone-mapping style also differs from application to application. In this paper, a learning-based multimodal tone-mapping method is proposed, which not only achieves excellent visual quality but also explores the style diversity. Based on the framework of BicycleGAN, the proposed method can provide a variety of expert-level tone-mapped results by manipulating different latent codes. Finally, we show that the proposed method performs favorably against state-of-the-art tone-mapping algorithms both quantitatively and qualitatively.

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          Author and article information

          Journal
          20 October 2020
          Article
          2010.10000
          9aea35cc-89ba-4c63-a2c5-81b3eeef5e42

          http://arxiv.org/licenses/nonexclusive-distrib/1.0/

          History
          Custom metadata
          To appear in ICPR 2020
          cs.CV

          Computer vision & Pattern recognition
          Computer vision & Pattern recognition

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