DreamClear: High-Capacity Real-World Image Restoration with Privacy-Safe
  Dataset Curation
Image restoration (IR) in real-world scenarios presents significant
challenges due to the lack of high-capacity models and comprehensive datasets.
To tackle these issues, we present a dual strategy: GenIR, an innovative data
curation pipeline, and DreamClear, a cutting-edge Diffusion Transformer
(DiT)-based image restoration model. GenIR, our pioneering contribution, is a
dual-prompt learning pipeline that overcomes the limitations of existing
datasets, which typically comprise only a few thousand images and thus offer
limited generalizability for larger models. GenIR streamlines the process into
three stages: image-text pair construction, dual-prompt based fine-tuning, and
data generation & filtering. This approach circumvents the laborious data
crawling process, ensuring copyright compliance and providing a cost-effective,
privacy-safe solution for IR dataset construction. The result is a large-scale
dataset of one million high-quality images. Our second contribution,
DreamClear, is a DiT-based image restoration model. It utilizes the generative
priors of text-to-image (T2I) diffusion models and the robust perceptual
capabilities of multi-modal large language models (MLLMs) to achieve
photorealistic restoration. To boost the model's adaptability to diverse
real-world degradations, we introduce the Mixture of Adaptive Modulator (MoAM).
It employs token-wise degradation priors to dynamically integrate various
restoration experts, thereby expanding the range of degradations the model can
address. Our exhaustive experiments confirm DreamClear's superior performance,
underlining the efficacy of our dual strategy for real-world image restoration.
Code and pre-trained models will be available at:
https://github.com/shallowdream204/DreamClear.
671c719d15313d2c0d58079c
Yuang Ai
claimed_verified
2024-10-30T09:54:41.552Z
65709464b3501cbcb8f4007e
/avatars/4fb7524cb21e38138b4c67ff5501b403.svg
Yuang Ai
user
shallowdream204
671c719d15313d2c0d58079d
Xiaoqiang Zhou
671c719d15313d2c0d58079e
Huaibo Huang
671c719d15313d2c0d58079f
Xiaotian Han
claimed_verified
2024-10-30T09:54:43.229Z
650dde4ce14eeb01d42b37a1
https://cdn-avatars.huggingface.co/v1/production/uploads/650dde4ce14eeb01d42b37a1/n5Yv24uofZ2XJjXdYCrKd.png
Xiaotian Han
user
xiaotianhan
671c719d15313d2c0d5807a0
Zhengyu Chen
671c719d15313d2c0d5807a1
Quanzeng You
claimed_verified
2024-10-28T10:36:57.630Z
642bb8c87f152f6e72b8d3cd
https://cdn-avatars.huggingface.co/v1/production/uploads/642bb8c87f152f6e72b8d3cd/MXxTssocydbabGZ6XFIic.png
Quanzeng You
user
Ye27
671c719d15313d2c0d5807a2
Hongxia Yang
DreamClear: High-Capacity Real-World Image Restoration with Privacy-Safe
  Dataset Curation
Image restoration (IR) in real-world scenarios presents significant
challenges due to the lack of high-capacity models and comprehensive datasets.
To tackle these issues, we present a dual strategy: GenIR, an innovative data
curation pipeline, and DreamClear, a cutting-edge Diffusion Transformer
(DiT)-based image restoration model. GenIR, our pioneering contribution, is a
dual-prompt learning pipeline that overcomes the limitations of existing
datasets, which typically comprise only a few thousand images and thus offer
limited generalizability for larger models. GenIR streamlines the process into
three stages: image-text pair construction, dual-prompt based fine-tuning, and
data generation & filtering. This approach circumvents the laborious data
crawling process, ensuring copyright compliance and providing a cost-effective,
privacy-safe solution for IR dataset construction. The result is a large-scale
dataset of one million high-quality images. Our second contribution,
DreamClear, is a DiT-based image restoration model. It utilizes the generative
priors of text-to-image (T2I) diffusion models and the robust perceptual
capabilities of multi-modal large language models (MLLMs) to achieve
photorealistic restoration. To boost the model's adaptability to diverse
real-world degradations, we introduce the Mixture of Adaptive Modulator (MoAM).
It employs token-wise degradation priors to dynamically integrate various
restoration experts, thereby expanding the range of degradations the model can
address. Our exhaustive experiments confirm DreamClear's superior performance,
underlining the efficacy of our dual strategy for real-world image restoration.
Code and pre-trained models will be available at:
https://github.com/shallowdream204/DreamClear.
A dual strategy involving a data curation pipeline (GenIR) and a DiT-based model (DreamClear) enhances image restoration by creating a large dataset and utilizing generative priors and adaptive modulators.
Diffusion Transformer (DiT) dual-prompt learning image-text pair construction dual-prompt based fine-tuning data generation & filtering text-to-image (T2I) diffusion models multi-modal large language models (MLLMs) Mixture of Adaptive Modulator (MoAM) token-wise degradation priors