Unsupervised HDR Image and Video Tone Mapping via Contrastive Learning

TL;DR

This paper introduces IVTMNet, an unsupervised deep learning framework for HDR image and video tone mapping that leverages contrastive learning and novel modules to improve quality and temporal consistency.

Contribution

It proposes a unified unsupervised approach with contrastive loss, new modules for spatial and temporal feature enhancement, and a large-scale unpaired HDR-LDR video dataset.

Findings

Outperforms state-of-the-art methods in tone mapping quality.

Effectively maintains temporal consistency in videos.

Demonstrates the effectiveness of contrastive learning in unsupervised tone mapping.

Abstract

Capturing high dynamic range (HDR) images (videos) is attractive because it can reveal the details in both dark and bright regions. Since the mainstream screens only support low dynamic range (LDR) content, tone mapping algorithm is required to compress the dynamic range of HDR images (videos). Although image tone mapping has been widely explored, video tone mapping is lagging behind, especially for the deep-learning-based methods, due to the lack of HDR-LDR video pairs. In this work, we propose a unified framework (IVTMNet) for unsupervised image and video tone mapping. To improve unsupervised training, we propose domain and instance based contrastive learning loss. Instead of using a universal feature extractor, such as VGG to extract the features for similarity measurement, we propose a novel latent code, which is an aggregation of the brightness and contrast of extracted features, to measure the similarity of different pairs. We totally construct two negative pairs and three positive pairs to constrain the latent codes of tone mapped results. For the network structure, we propose a spatial-feature-enhanced (SFE) module to enable information exchange and transformation of nonlocal regions. For video tone mapping, we propose a temporal-feature-replaced (TFR) module to efficiently utilize the temporal correlation and improve the temporal consistency of video tone-mapped results. We construct a large-scale unpaired HDR-LDR video dataset to facilitate the unsupervised training process for video tone mapping. Experimental results demonstrate that our method outperforms state-of-the-art image and video tone mapping methods. Our code and dataset are available at https://github.com/cao-cong/UnCLTMO.