  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.
