Vaishnav Potlapalli, Syed Waqas Zamir, Salman Khan and Fahad Shahbaz Khan
Abstract: Image restoration involves recovering a high-quality clean image from its degraded version. Deep learning-based methods have significantly improved image restora- tion performance, however, they have limited generalization ability to different degradation types and levels. This restricts their real-world application since it requires training individual models for each specific degradation and knowing the input degradation type to apply the relevant model. We present a prompt-based learning approach, PromptIR, for All-In-One image restoration that can effectively restore images from various types and levels of degradation. In particular, our method uses prompts to encode degradation-specific information, which is then used to dynamically guide the restoration network. This allows our method to generalize to different degradation types and levels, while still achieving state-of- the-art results on image denoising, deraining, and dehazing. Overall, PromptIR offers a generic and efficient plugin module with few lightweight prompts that can be used to restore images of various types and levels of degradation with no prior information of corruptions.
See INSTALL.md for the installation of dependencies and dataset preperation required to run this codebase.
After preparing the training data in data/ directory, use
python train.py
to start the training of the model. Use the de_type argument to choose the combination of degradation types to train on. By default it is set to all the 3 degradation types (noise, rain, and haze).
Example Usage: If we only want to train on deraining and dehazing:
python train.py --de_type derain dehaze
After preparing the testing data in test/ directory, place the mode checkpoint file in the ckpt directory. The pretrained model can be downloaded here, alternatively, it is also available under the releases tab. To perform the evalaution use
python test.py --mode {n}
n is a number that can be used to set the tasks to be evaluated on, 0 for denoising, 1 for deraining, 2 for dehaazing and 3 for all-in-one setting.
Example Usage: To test on all the degradation types at once, run:
python test.py --mode 3
To obtain visual results from the model demo.py can be used. After placing the saved model file in ckpt directory, run:
python demo.py --test_path {path_to_degraded_images} --output_path {save_images_here}
Example usage to run inference on a directory of images:
python demo.py --test_path './test/demo/' --output_path './output/demo/'
Example usage to run inference on an image directly:
python demo.py --test_path './test/demo/image.png' --output_path './output/demo/'
To use tiling option while running demo.py set --tile option to True. The Tile size and Tile overlap parameters can be adjusted using --tile_size and --tile_overlap options respectively.
Performance results of the PromptIR framework trained under the all-in-one setting
The visual results of the PromptIR model evaluated under the all-in-one setting can be downloaded here
If you use our work, please consider citing:
@inproceedings{potlapalli2023promptir,
title={PromptIR: Prompting for All-in-One Image Restoration},
author={Potlapalli, Vaishnav and Zamir, Syed Waqas and Khan, Salman and Khan, Fahad},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023}
}
Should you have any questions, please contact [email protected]
Acknowledgment: This code is based on the AirNet and Restormer repositories.
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