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As India’s National Broadcaster and also the premier Public Service Broadcaster, All India Radio (AIR) has been serving to inform, educate and entertain the masses since it’s inception, truly living up to its motto – ‘Bahujan Hitaya : Bahujan Sukhaya’.
AIRnet offers a complete and efficient compressed air distribution system from start to finish. Find the right material, design tools, and after-sales support for your application and industry.
AirNet Networks offers fast, secure and reliable Internet services in Mumbai, Goa and Pune. It has a fiber optics network, a dedicated support team and a customer satisfaction guarantee.
On June 8, 1936, the Indian State Broadcasting Service became All India Radio. The Central News Organisation (CNO) came into existence in August, 1937. In the same year, AIR came under the Department of Communications and four years later came under the Department of Information and Broadcasting.
- Overview
- Dependencies
- Dataset
- Demo
- Training
- Testing
- Citation
- Acknowledgement
PyTorch implementation for All-In-One Image Restoration for Unknown Corruption (AirNet) (CVPR 2022). [paper]
•Python == 3.8.11
•Pytorch == 1.7.0
•mmcv-full == 1.3.11
We also export our conda virtual environment as airnet.yaml. You can use the following command to create the environment.
You could find the dataset we used in the paper at following:
Denoising: BSD400, WED, Urban100
Deraining: Train100L&Rain100L
Dehazing: RESIDE (OTS)
You could download the pre-trained model from Google Drive and Baidu Netdisk (password: cr7d). Remember to put the pre-trained model into ckpt/
If you only need the visual results, you could put the test images into test/demo/ and use the following command to restore the test image:
If you want to re-train our model, you need to first put the training set into the data/, and use the following command:
ps. To train with different combinations of corruptions, you could modify the "de_type" in option.py.
If you want to test our model and get the psnr and ssim, you need to put the testing set into the test/, where several examples are given. Then, you could use the following command:
where mode == 3 means we use the checkpoint trained on all-in-one setting. (0 for denoising, 1 for deraining and 2 for dehazing)
If you find AirNet useful in your research, please consider citing:
This repo is built upon the framework of DASR, and we borrow some code from mmcv, thanks for their excellent work!
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