High-Quality Image Restoration Following Human Instructions
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
65b9ab75efa633aa9a891d26
Marcos V. Conde
admin_assigned
2024-01-31T10:19:38.480Z
634169d1a7582111c3f46bae
https://cdn-avatars.huggingface.co/v1/production/uploads/634169d1a7582111c3f46bae/z5V5c1a7FRsRWnCFZbtUu.jpeg
Marcos V. Conde
user
marcosv
65b9ab75efa633aa9a891d27
Gregor Geigle
admin_assigned
2024-01-31T10:19:46.002Z
613f291fe5a812eff913808e
https://cdn-avatars.huggingface.co/v1/production/uploads/613f291fe5a812eff913808e/aQUoRv6tllC2j-jg6meUt.jpeg
Gregor Geigle
user
Gregor
65b9ab75efa633aa9a891d28
Radu Timofte
High-Quality Image Restoration Following Human Instructions
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
InstructIR is an all-in-one image restoration method that leverages human-written instructions to guide the restoration process, achieving state-of-the-art results across multiple tasks.
All-In-One image restoration natural language prompts image denoising deraining deblurring dehazing low-light image enhancement text-guided image restoration