  This paper introduces a new large-scale image restoration dataset, called
HQ-50K, which contains 50,000 high-quality images with rich texture details and
semantic diversity. We analyze existing image restoration datasets from five
different perspectives, including data scale, resolution, compression rates,
texture details, and semantic coverage. However, we find that all of these
datasets are deficient in some aspects. In contrast, HQ-50K considers all of
these five aspects during the data curation process and meets all requirements.
We also present a new Degradation-Aware Mixture of Expert (DAMoE) model, which
enables a single model to handle multiple corruption types and unknown levels.
Our extensive experiments demonstrate that HQ-50K consistently improves the
performance on various image restoration tasks, such as super-resolution,
denoising, dejpeg, and deraining. Furthermore, our proposed DAMoE, trained on
our \dataset, outperforms existing state-of-the-art unified models designed for
multiple restoration tasks and levels. The dataset and code are available at
\url{https://github.com/littleYaang/HQ-50K}.
