论文标题
可探索的音调映射操作员
Explorable Tone Mapping Operators
论文作者
论文摘要
音调映射在高动态范围（HDR）成像中起着至关重要的作用。它旨在将HDR图像的视觉信息保存在动态范围有限的介质中。尽管已经提出了许多作品来提供来自HDR图像的音调映射结果，但其中大多数只能以单个预设计的方式执行音调映射。但是，音调映射质量的主观性因人而异，而映射样式的偏好也因应用程序而异。在本文中，提出了一种基于学习的多模式图映射方法，该方法不仅可以达到出色的视觉质量，而且还探索了风格的多样性。基于Bicyclegan的框架，提出的方法可以通过操纵不同的潜在代码来提供各种专家级图形映射结果。最后，我们表明该提出的方法在定量和定性上都针对最新的音调映射算法表现出色。
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.
