  In the image acquisition process, various forms of degradation, including
noise, 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 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 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 achieves state-of-the-art performance on different
image restoration tasks, including denoising, dehazing, deraining, motion
deblurring, and low-light image enhancement. Our code is available at
https://github.com/c-yn/AdaIR.
