ADAIR: adaptive all-in-one image restoration via frequency mining and modulation
Cui, Yuning Zamir, Syed Waqas Khan, Salman Knoll, Alois Shah, Mubarak Khan, Fahad
Cui, Yuning
Zamir, Syed Waqas
Khan, Salman
Knoll, Alois
Shah, Mubarak
Khan, Fahad
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Department
Computer Vision
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Conference proceeding
Date
2025
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English
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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, most 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, AdaIR, achieves state-of-the-art performance on different image restoration tasks, including image denoising, dehazing, deraining, motion deblurring, and low-light image enhancement. The code is available at https://github.com/c-yn/AdaIR. © 2025 13th International Conference on Learning Representations, ICLR 2025. All rights reserved.
Citation
Y. Cui, S. Waqas Zamir, S. Khan, A. Knoll, M. Shah, and F. Shahbaz Khan, “AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and Modulation,” International Conference on Representation Learning, vol. 2025, pp. 101306–101327, May 2025
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13th International Conference on Learning Representations, ICLR 2025
Conference
13th International Conference on Learning Representations, ICLR 2025
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Source
13th International Conference on Learning Representations, ICLR 2025
Publisher
International Conference on Learning Representations, ICLR