Explorable tone mapping operators
Journal
Proceedings - International Conference on Pattern Recognition
Pages
10320-10326
Date Issued
2020
Author(s)
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 [1], 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. © 2020 IEEE
Other Subjects
Conformal mapping; High dynamic range; Limited dynamic ranges; Multi-modal; State of the art; Tone mapping; Tone mapping operators; Visual information; Visual qualities; Pattern recognition
Type
conference paper