Yuning Cui, Syed Waqas Zamir, Salman Khan, Alois Knoll, Mubarak Shah, and Fahad Shahbaz Khan

Abstract: In the image acquisition process, various forms of degradation, including noise, blur, haze, and rain, are frequently introduced. These degradations typically arise from the inherent limitations of cameras or unfavorable ambient conditions. To recover clean images from their degraded versions, numerous specialized restoration methods have been developed, each targeting a specific type of degradation. Recently, all-in-one algorithms have garnered significant attention by addressing different types of degradations within a single model without requiring the prior information of the input degradation type. However, these methods purely operate in the spatial domain and do not delve into the distinct frequency variations inherent to different degradation types. To address this gap, we propose an adaptive all-in-one image restoration network based on frequency mining and modulation. Our approach is motivated by the observation that different degradation types impact the image content on different frequency subbands, thereby requiring different treatments for each restoration task. Specifically, we first mine low- and high-frequency information from the input features, guided by the adaptively decoupled spectra of the degraded image. The extracted features are then modulated by a bidirectional operator to facilitate interactions between different frequency components. Finally, the modulated features are merged into the original input for a progressively guided restoration. With this approach, the model achieves adaptive reconstruction by accentuating the informative frequency subbands according to different input degradations. Extensive experiments demonstrate that the proposed method, named AdaIR, achieves state-of-the-art performance on different image restoration tasks, including image denoising, dehazing, deraining, motion deblurring, and low-light image enhancement.

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 5 degradation tasks (denoising, deraining, dehazing, deblurring, enhancement).

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 pre-trained model can be downloaded here. To perform the evaluation, 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 dehazing, 3 for deblurring, 4 for enhancement, 5 for three-degradation all-in-one setting and 6 for five-degradation all-in-one setting.

Example Usage: To test on all the degradation types at once, run:

python test.py --mode 6

Performance results of the AdaIR framework trained under the all-in-one setting.

Three Distinct Degradations (click to expand)

Five Distinct Degradations (click to expand)

The visual results can be downloaded here.

If you use our work, please consider citing:

@misc{cui2024adair,
      title={AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and Modulation}, 
      author={Yuning Cui and Syed Waqas Zamir and Salman Khan and Alois Knoll and Mubarak Shah and Fahad Shahbaz Khan},
      year={2024},
      eprint={2403.14614},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Should you have any questions, please contact [email protected]

Acknowledgment: This code is based on the PromptIR repository.