JZD151/Real-IISR

[CVPR 2026 Highlight] Official implementation for "Toward Real-world Infrared Image Super-Resolution: A Unified Autoregressive Framework and Benchmark Dataset".

57

stars

58

commits

Python

primary language

Jul 20, 2026

updated

README

Teaser

Toward Real-world Infrared Image Super-Resolution: A Unified Autoregressive Framework and Benchmark Dataset

Yang Zou, Jun Ma, Zhidong Jiao, Xingyuan Li, Zhiying Jiang, and Jinyuan Liu, "Toward Real-world Infrared Image Super-Resolution: A Unified Autoregressive Framework and Benchmark Dataset", CVPR 2026 Highlight

:rocket: Updates

[2026-3-10] Our training code and inference code is now available.🎊🎊🎊

[2026-3-6] You can find our paper here. ⭐️⭐️⭐️

[2026-3-4] Our dataset is now available.🔥🔥🔥

[2026-2-21] Our paper has been accepted by CVPR 2026. The code and dataset have been officially released.🎉🎉🎉

💾 FLIR-IISR Dataset 💾

:open_book: Dataset Details

Download

Composition ($1457$ pairs)

  • Scene labels ($12$ categories):

    • person ($309$), bicycle ($22$), motorcycle ($27$), tricycle ($13$), car ($234$), bus ($5$) plane ($54$), statue ($157$), regular object ($248$), building ($706$), road ($132$), and complex scene ($401$).
  • Degradation labels:

    • Optical blur ($1305$); Motion blur ($152$).
  • Total number of image pairs: $1457$

  • Image size: $1024 \times 768$

Preview


Scene labels:


person (309)

bicycle (22)

motorcycle (27)

tricycle (13)

car (234)

bus (5)

plane (54)

statue (157)

regular object (248)

building (706)

road (132)

complex scene (401)

Degradation labels:


Optical blur (1305)

Optical blur (1305)

Motion blur (152)

Motion blur (152)

📦 Real-IISR 📦

Pipeline

⚙️ Dependencies

git clone https://github.com/JZD151/Real-IISR.git
cd Real-IISR

conda create -n Real-IISR python=3.10
conda activate Real-IISR
pip install -r requirements.txt
pip install flash_attn-2.7.4.post1 --no-build-isolation

🔧 Training

  1. Download the pretrained VQVAE and VARSR models, and place them in the ./checkpoints directory.
  2. Download the FLIR-IISR dataset and extract it.
python train.py --batch_size 4 --ep 20 --fp16 1 --tblr 5e-5 --alng 1e-4 --wpe 0.01 --fuse 0 --exp_name Real-IISR

🔨 Testing

Note: We provide several sample inputs for easy inference.

  1. Download the pretrained model from 百度网盘 / HuggingFace , and place it in the ./checkpoints directory.
python test.py

📎 Citation

@inproceedings{zou2026toward,
  title={Toward real-world infrared image super-resolution: A unified autoregressive framework and benchmark dataset},
  author={Zou, Yang and Ma, Jun and Jiao, Zhidong and Li, Xingyuan and Jiang, Zhiying and Liu, Jinyuan},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={16365--16375},
  year={2026}
}

📫 Contact

If you have any questions, feel free to contact us through archerv2@mail.nwpu.edu.cn.

💡 Acknowledgements

Our codes are based on VAR, VARSR, thanks for their contribution.

Contributors

Junma812

42 commits

JZD151

15 commits

jiaozhidong

1 commits

JZD151/Real-IISR

[CVPR 2026 Highlight] Official implementation for "Toward Real-world Infrared Image Super-Resolution: A Unified Autoregressive Framework and Benchmark Dataset".

57

stars

58

commits

Python

primary language

Jul 20, 2026

updated

README

Teaser

Toward Real-world Infrared Image Super-Resolution: A Unified Autoregressive Framework and Benchmark Dataset

Yang Zou, Jun Ma, Zhidong Jiao, Xingyuan Li, Zhiying Jiang, and Jinyuan Liu, "Toward Real-world Infrared Image Super-Resolution: A Unified Autoregressive Framework and Benchmark Dataset", CVPR 2026 Highlight

:rocket: Updates

[2026-3-10] Our training code and inference code is now available.🎊🎊🎊

[2026-3-6] You can find our paper here. ⭐️⭐️⭐️

[2026-3-4] Our dataset is now available.🔥🔥🔥

[2026-2-21] Our paper has been accepted by CVPR 2026. The code and dataset have been officially released.🎉🎉🎉

💾 FLIR-IISR Dataset 💾

:open_book: Dataset Details

Download

Composition ($1457$ pairs)

  • Scene labels ($12$ categories):

    • person ($309$), bicycle ($22$), motorcycle ($27$), tricycle ($13$), car ($234$), bus ($5$) plane ($54$), statue ($157$), regular object ($248$), building ($706$), road ($132$), and complex scene ($401$).
  • Degradation labels:

    • Optical blur ($1305$); Motion blur ($152$).
  • Total number of image pairs: $1457$

  • Image size: $1024 \times 768$

Preview


Scene labels:


person (309)

bicycle (22)

motorcycle (27)

tricycle (13)

car (234)

bus (5)

plane (54)

statue (157)

regular object (248)

building (706)

road (132)

complex scene (401)

Degradation labels:


Optical blur (1305)

Optical blur (1305)

Motion blur (152)

Motion blur (152)

📦 Real-IISR 📦

Pipeline

⚙️ Dependencies

git clone https://github.com/JZD151/Real-IISR.git
cd Real-IISR

conda create -n Real-IISR python=3.10
conda activate Real-IISR
pip install -r requirements.txt
pip install flash_attn-2.7.4.post1 --no-build-isolation

🔧 Training

  1. Download the pretrained VQVAE and VARSR models, and place them in the ./checkpoints directory.
  2. Download the FLIR-IISR dataset and extract it.
python train.py --batch_size 4 --ep 20 --fp16 1 --tblr 5e-5 --alng 1e-4 --wpe 0.01 --fuse 0 --exp_name Real-IISR

🔨 Testing

Note: We provide several sample inputs for easy inference.

  1. Download the pretrained model from 百度网盘 / HuggingFace , and place it in the ./checkpoints directory.
python test.py

📎 Citation

@inproceedings{zou2026toward,
  title={Toward real-world infrared image super-resolution: A unified autoregressive framework and benchmark dataset},
  author={Zou, Yang and Ma, Jun and Jiao, Zhidong and Li, Xingyuan and Jiang, Zhiying and Liu, Jinyuan},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={16365--16375},
  year={2026}
}

📫 Contact

If you have any questions, feel free to contact us through archerv2@mail.nwpu.edu.cn.

💡 Acknowledgements

Our codes are based on VAR, VARSR, thanks for their contribution.

Contributors

Junma812

42 commits

JZD151

15 commits

jiaozhidong

1 commits

Languages

Python

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