---
pretty_name: IRVAL
license: cc-by-4.0
language:
- en
task_categories:
- video-to-video
tags:
- infrared
- thermal
- lwir
- video
- computer-vision
configs:
- config_name: default
  data_files:
  - split: train
    path: IRVAL/videos/*.avi
---

# IRVAL

IRVAL is a high-resolution infrared video dataset for infrared video processing and spatial-temporal video super-resolution research.

## Dataset Summary

This repository currently provides 8 infrared videos in `.avi` format.

According to our associated paper, IRVAL is a high-resolution infrared dataset comprising 108,512 video frames at a spatial resolution of 512×512. The data are collected using vanadium oxide (VOx) uncooled focal plane array detectors operating in the long-wave infrared (LWIR) band. The videos are captured from both vehicle-mounted and fixed surveillance platforms, covering real-world scenarios such as urban streets, vehicles, pedestrians, and roadside buildings.

## Repository Structure

```text
.
├── README.md
├── IRVAL/
│   └── videos/
│       ├── irval_seq01.avi
│       ├── irval_seq02.avi
│       ├── irval_seq03.avi
│       ├── irval_seq04.avi
│       ├── irval_seq05.avi
│       ├── irval_seq06.avi
│       ├── irval_seq07.avi
│       └── irval_seq08.avi
└── .gitattributes
```

## Intended Use

This dataset is intended for research on:

- infrared video processing
- infrared video super-resolution
- spatial-temporal video super-resolution
- temporal consistency modeling

## Notes

- This release currently contains 8 raw infrared videos.
- The current video path used by the dataset viewer is `IRVAL/videos/*.avi`.
- Users can generate task-specific LR/HR training pairs following their own protocol or the protocol described in the associated paper.
- This dataset is intended for research use only.

## Citation

If you use this dataset, please cite:

```bibtex
@inproceedings{zhou2026thermal,
  title={Thermal Diffusion Matters: Infrared Spatial-Temporal Video Super-Resolution through Heat Conduction Priors},
  author={Mingxuan Zhou and Shuang Li and Yutang Zhang and Jing Geng and Yirui Shen and Jingxuan Kang and Fuzhen Zhuang and Shuigen Wang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2026}
}
```