# Explorable Tone Mapping Operators

arXiv: https://arxiv.org/abs/2010.10000 · HTML: https://mlsanity.com/paper/2010.10000/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [Learning to Adapt to Light](https://mlsanity.com/paper/2202.08098/) (2022)
- [Learning a self-supervised tone mapping operator via feature contrast masking loss](https://mlsanity.com/paper/2110.09866/) (2021)
- [Learning Adaptive Lighting via Channel-Aware Guidance](https://mlsanity.com/paper/2412.01493/) (2024)
- [Joint tone mapping and denoising of thermal infrared images via multi-scale Retinex and multi-task learning](https://mlsanity.com/paper/2305.00691/) (2023)
- [A review for Tone-mapping Operators on Wide Dynamic Range Image](https://mlsanity.com/paper/2101.03003/) (2021)
- [Joint denoising and HDR for RAW video sequences](https://mlsanity.com/paper/2201.07066/) (2022)

## Abstract (arXiv)

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.