Create README.md

README.md

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+---
+pretty_name: IRVAL
+license: cc-by-4.0
+task_categories:
+- video-super-resolution
+tags:
+- infrared
+- thermal
+- lwir
+- video
+- video-super-resolution
+- computer-vision
+language:
+- en
+
+---
+
+# 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 contains 8 long infrared videos in `.avi` format.
+
+According to the 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
+IRVAL/
+├── README.md
+├── LICENSE
+└── 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
+```
+
+## Data Format
+
+- Format: `.avi`
+- Modality: thermal infrared video
+- Resolution: 512×512
+- Sensor type: VOx uncooled focal plane array detector
+- Spectral band: long-wave infrared (LWIR)
+
+## 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 contains 8 long infrared videos.
+- This repository releases raw videos rather than pre-generated LR/HR pairs.
+- Please make sure you comply with the dataset license and all applicable usage restrictions.
+
+## Citation
+
+If you use this dataset, please cite:
+
+```
+@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 (CVPR)},
+  year={2026}
+}
+```
+
+## Contact
+
+For questions about the dataset, please contact: mingxuanzhou@bit.edu.cn
+