# HDRT: A Large-Scale Dataset for Infrared-Guided HDR Imaging

arXiv: https://arxiv.org/abs/2406.05475 · HTML: https://mlsanity.com/paper/2406.05475/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 64 references that are in the map.
- First posted on arXiv 8 Jun 2024.

## Builds on (most cited first, 24 of 24)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection](https://mlsanity.com/paper/2203.16220/) (2022)
- [LLVIP: A Visible-infrared Paired Dataset for Low-light Vision](https://mlsanity.com/paper/2108.10831/) (2021)
- [HDR image reconstruction from a single exposure using deep CNNs](https://mlsanity.com/paper/1710.07480/) (2017)
- [Multi-interactive Feature Learning and a Full-time Multi-modality Benchmark for Image Fusion and Segmentation](https://mlsanity.com/paper/2308.02097/) (2023)
- [RGB-T Object Tracking:Benchmark and Baseline](https://mlsanity.com/paper/1805.08982/) (2018)
- [Attention-guided Network for Ghost-free High Dynamic Range Imaging](https://mlsanity.com/paper/1904.10293/) (2019)
- [Equivariant Multi-Modality Image Fusion](https://mlsanity.com/paper/2305.11443/) (2023)
- [LasHeR: A Large-scale High-diversity Benchmark for RGBT Tracking](https://mlsanity.com/paper/2104.13202/) (2021)
- [Visible-Thermal UAV Tracking: A Large-Scale Benchmark and New Baseline](https://mlsanity.com/paper/2204.04120/) (2022)
- [ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content](https://mlsanity.com/paper/1803.02266/) (2018)
- [Single Image HDR Reconstruction Using a CNN with Masked Features and Perceptual Loss](https://mlsanity.com/paper/2005.07335/) (2020)
- [Ghost-free High Dynamic Range Imaging with Context-aware Transformer](https://mlsanity.com/paper/2208.05114/) (2022)
- [HDRUNet: Single Image HDR Reconstruction with Denoising and Dequantization](https://mlsanity.com/paper/2105.13084/) (2021)
- [Jointly Modeling Motion and Appearance Cues for Robust RGB-T Tracking](https://mlsanity.com/paper/2007.02041/) (2020)
- [Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applications](https://mlsanity.com/paper/1606.03237/) (2016)
- [NTIRE 2021 Challenge on High Dynamic Range Imaging: Dataset, Methods and Results](https://mlsanity.com/paper/2106.01439/) (2021)
- [Alignment-free HDR Deghosting with Semantics Consistent Transformer](https://mlsanity.com/paper/2305.18135/) (2023)
- [A New Journey from SDRTV to HDRTV](https://mlsanity.com/paper/2108.07978/) (2021)
- [GlowGAN: Unsupervised Learning of HDR Images from LDR Images in the Wild](https://mlsanity.com/paper/2211.12352/) (2022)
- [Luminance Attentive Networks for HDR Image and Panorama Reconstruction](https://mlsanity.com/paper/2109.06688/) (2021)
- [High Dynamic Range and Super-Resolution from Raw Image Bursts](https://mlsanity.com/paper/2207.14671/) (2022)
- [Siamese Infrared and Visible Light Fusion Network for RGB-T Tracking](https://mlsanity.com/paper/2103.07302/) (2021)
- [Dynamic Fusion Network for RGBT Tracking](https://mlsanity.com/paper/2109.07662/) (2021)

## Built on by (most cited first, 4 of 4)

- [DUALVISION: RGB-Infrared Multimodal Large Language Models for Robust Visual Reasoning](https://mlsanity.com/paper/2604.18829/) (2026)
- [CapHDR2IR: Caption-Driven Transfer from Visible Light to Infrared Domain](https://mlsanity.com/paper/2411.16327/) (2024)
- [Recurrent Dynamic Range Extension](https://mlsanity.com/paper/2609.13135/) (2026)
- [High Dynamic Range Video Reconstruction from Single-Exposure Raw Sequences](https://mlsanity.com/paper/2609.27274/) (2026)

## Abstract (arXiv)

Capturing images with enough details to solve imaging tasks is a long-standing challenge in imaging, particularly due to the limitations of standard dynamic range (SDR) images which often lose details in underexposed or overexposed regions. Traditional high dynamic range (HDR) methods, like multi-exposure fusion or inverse tone mapping, struggle with ghosting and incomplete data reconstruction. Infrared (IR) imaging offers a unique advantage by being less affected by lighting conditions, providing consistent detail capture regardless of visible light intensity. In this paper, we introduce the HDRT dataset, the first comprehensive dataset that consists of HDR and thermal IR images. The HDRT dataset comprises 50,000 images captured across three seasons over six months in eight cities, providing a diverse range of lighting conditions and environmental contexts. Leveraging this dataset, we propose HDRTNet, a novel deep neural method that fuses IR and SDR content to generate HDR images. Extensive experiments validate HDRTNet against the state-of-the-art, showing substantial quantitative and qualitative quality improvements. The HDRT dataset not only advances IR-guided HDR imaging but also offers significant potential for broader research in HDR imaging, multi-modal fusion, domain transfer, and beyond. The dataset is available at https://huggingface.co/datasets/jingchao-peng/HDRTDataset.