
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
[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 💾
Scene labels ($12$ categories):
Degradation labels:
Total number of image pairs: $1457$
Image size: $1024 \times 768$
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 📦

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
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
Note: We provide several sample inputs for easy inference.
python test.py
@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}
}
If you have any questions, feel free to contact us through archerv2@mail.nwpu.edu.cn.
Our codes are based on VAR, VARSR, thanks for their contribution.
Python
100.0%

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
[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 💾
Scene labels ($12$ categories):
Degradation labels:
Total number of image pairs: $1457$
Image size: $1024 \times 768$
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 📦

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
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
Note: We provide several sample inputs for easy inference.
python test.py
@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}
}
If you have any questions, feel free to contact us through archerv2@mail.nwpu.edu.cn.
Our codes are based on VAR, VARSR, thanks for their contribution.
Python
100.0%