DreamClear: High-Capacity Real-World Image Restoration with Privacy-Safe
  Dataset Curation
Authors: name: Yuang Ai, name: Xiaoqiang Zhou, name: Huaibo Huang, name: Xiaotian Han, name: Zhengyu Chen, name: Quanzeng You, name: Hongxia Yang

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.