title: Adaptive Blind All-in-One Image Restoration
summary: Blind all-in-one image restoration models aim to recover a high-quality image
  from an input degraded with unknown distortions. However, these models require
  all the possible degradation types to be defined during the training stage
  while showing limited generalization to unseen degradations, which limits their
  practical application in complex cases. In this paper, we propose a simple but
  effective adaptive blind all-in-one restoration (ABAIR) model, which can
  address multiple degradations, generalizes well to unseen degradations, and
  efficiently incorporate new degradations by training a small fraction of
  parameters. First, we train our baseline model on a large dataset of natural
  images with multiple synthetic degradations, augmented with a segmentation head
  to estimate per-pixel degradation types, resulting in a powerful backbone able
  to generalize to a wide range of degradations. Second, we adapt our baseline
  model to varying image restoration tasks using independent low-rank adapters.
  Third, we learn to adaptively combine adapters to versatile images via a
  flexible and lightweight degradation estimator. Our model is both powerful in
  handling specific distortions and flexible in adapting to complex tasks, it not
  only outperforms the state-of-the-art by a large margin on five- and three-task
  IR setups, but also shows improved generalization to unseen degradations and
  also composite distortions.
paper_authors._id: 67483446e4a6e7b8a5223991
paper_authors.name: David Serrano-Lozano
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-11-28T09:29:39.301Z
paper_authors._id: 6479b418bfaa9e96b850959b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6479b418bfaa9e96b850959b/qQSSgCK2M8ATutL6H0Yvv.jpeg
paper_authors.fullname: David Serrano Lozano
paper_authors.type: user
paper_authors.user: davidserra9
paper_authors._id: 67483446e4a6e7b8a5223992
paper_authors.name: Luis Herranz
paper_authors._id: 67483446e4a6e7b8a5223993
paper_authors.name: Shaolin Su
paper_authors._id: 67483446e4a6e7b8a5223994
paper_authors.name: Javier Vazquez-Corral
paper_title: Adaptive Blind All-in-One Image Restoration
paper_summary: Blind all-in-one image restoration models aim to recover a high-quality image
  from an input degraded with unknown distortions. However, these models require
  all the possible degradation types to be defined during the training stage
  while showing limited generalization to unseen degradations, which limits their
  practical application in complex cases. In this paper, we propose a simple but
  effective adaptive blind all-in-one restoration (ABAIR) model, which can
  address multiple degradations, generalizes well to unseen degradations, and
  efficiently incorporate new degradations by training a small fraction of
  parameters. First, we train our baseline model on a large dataset of natural
  images with multiple synthetic degradations, augmented with a segmentation head
  to estimate per-pixel degradation types, resulting in a powerful backbone able
  to generalize to a wide range of degradations. Second, we adapt our baseline
  model to varying image restoration tasks using independent low-rank adapters.
  Third, we learn to adaptively combine adapters to versatile images via a
  flexible and lightweight degradation estimator. Our model is both powerful in
  handling specific distortions and flexible in adapting to complex tasks, it not
  only outperforms the state-of-the-art by a large margin on five- and three-task
  IR setups, but also shows improved generalization to unseen degradations and
  also composite distortions.
paper_ai_summary: A proposed ABAIR model adapts to various image restorations by efficiently incorporating new degradations using low-rank adapters, achieving top performance and generalization.
paper_ai_keywords: blind all-in-one image restoration adaptive blind all-in-one restoration (ABAIR) segmentation head low-rank adapters degradation estimator image restoration unseen degradations composite distortions