Explorable Tone Mapping Operators

• URL: http://arxiv.org/abs/2010.10000v1
• Date: Tue, 20 Oct 2020 04:18:54 GMT
• Title: Explorable Tone Mapping Operators
• Authors: Chien-Chuan Su, Ren Wang, Hung-Jin Lin, Yu-Lun Liu, Chia-Ping Chen, Yu-Lin Chang and Soo-Chang Pei
• Abstract summary: A learning-based multimodal tone-mapping method is proposed, which achieves excellent visual quality and explores the style diversity. We show that the proposed method performs favorably against state-of-the-art tone-mapping algorithms both quantitatively and qualitatively.
• Score: 14.200830640747288
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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