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.
@article{arxiv.2010.10000,
title = {Explorable Tone Mapping Operators},
author = {Chien-Chuan Su and Ren Wang and Hung-Jin Lin and Yu-Lun Liu and Chia-Ping Chen and Yu-Lin Chang and Soo-Chang Pei},
journal= {arXiv preprint arXiv:2010.10000},
year = {2020}
}