title: AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and
    Modulation
summary: 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.
paper_authors._id: 67978e94d8e2dcea3de32387
paper_authors.name: Yuning Cui
paper_authors._id: 67978e94d8e2dcea3de32388
paper_authors.name: Syed Waqas Zamir
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-27T15:05:10.425Z
paper_authors._id: 6245a8cc4db06ca3fff5a4de
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1648732357931-noauth.jpeg
paper_authors.fullname: Syed Waqas Zamir
paper_authors.type: user
paper_authors.user: swzamir
paper_authors._id: 67978e94d8e2dcea3de32389
paper_authors.name: Salman Khan
paper_authors._id: 67978e94d8e2dcea3de3238a
paper_authors.name: Alois Knoll
paper_authors._id: 67978e94d8e2dcea3de3238b
paper_authors.name: Mubarak Shah
paper_authors._id: 67978e94d8e2dcea3de3238c
paper_authors.name: Fahad Shahbaz Khan
paper_title: AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and
    Modulation
paper_summary: 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.
paper_ai_keywords: frequency mining frequency modulation bidirectional operator frequency subbands low-frequency information high-frequency information adaptively decoupled spectra adaptive reconstruction progressively guided restoration