title: High-Quality Image Restoration Following Human Instructions
summary: Image restoration is a fundamental problem that involves recovering a
  high-quality clean image from its degraded observation. All-In-One image
  restoration models can effectively restore images from various types and levels
  of degradation using degradation-specific information as prompts to guide the
  restoration model. In this work, we present the first approach that uses
  human-written instructions to guide the image restoration model. Given natural
  language prompts, our model can recover high-quality images from their degraded
  counterparts, considering multiple degradation types. Our method, InstructIR,
  achieves state-of-the-art results on several restoration tasks including image
  denoising, deraining, deblurring, dehazing, and (low-light) image enhancement.
  InstructIR improves +1dB over previous all-in-one restoration methods.
  Moreover, our dataset and results represent a novel benchmark for new research
  on text-guided image restoration and enhancement. Our code, datasets and models
  are available at: https://github.com/mv-lab/InstructIR
paper_authors._id: 65b9ab75efa633aa9a891d26
paper_authors.name: Marcos V. Conde
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-31T10:19:38.480Z
paper_authors._id: 634169d1a7582111c3f46bae
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/634169d1a7582111c3f46bae/i4FleB3dcxksU4B9M_IRa.jpeg
paper_authors.fullname: Marcos V. Conde
paper_authors.type: user
paper_authors.user: marcosv
paper_authors._id: 65b9ab75efa633aa9a891d27
paper_authors.name: Gregor Geigle
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-31T10:19:46.002Z
paper_authors._id: 613f291fe5a812eff913808e
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/613f291fe5a812eff913808e/aQUoRv6tllC2j-jg6meUt.jpeg
paper_authors.fullname: Gregor Geigle
paper_authors.type: user
paper_authors.user: Gregor
paper_authors._id: 65b9ab75efa633aa9a891d28
paper_authors.name: Radu Timofte
paper_title: High-Quality Image Restoration Following Human Instructions
paper_summary: Image restoration is a fundamental problem that involves recovering a
  high-quality clean image from its degraded observation. All-In-One image
  restoration models can effectively restore images from various types and levels
  of degradation using degradation-specific information as prompts to guide the
  restoration model. In this work, we present the first approach that uses
  human-written instructions to guide the image restoration model. Given natural
  language prompts, our model can recover high-quality images from their degraded
  counterparts, considering multiple degradation types. Our method, InstructIR,
  achieves state-of-the-art results on several restoration tasks including image
  denoising, deraining, deblurring, dehazing, and (low-light) image enhancement.
  InstructIR improves +1dB over previous all-in-one restoration methods.
  Moreover, our dataset and results represent a novel benchmark for new research
  on text-guided image restoration and enhancement. Our code, datasets and models
  are available at: https://github.com/mv-lab/InstructIR
paper_ai_keywords: image restoration contamination prompts human-written instructions text-guided image restoration denoising deraining deblurring dehazing low-light enhancement dataset benchmark