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HQ-50K: A Large-scale, High-quality Dataset for Image Restoration

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arxiv 2306.05390 v1 pith:5JNTIQVM submitted 2023-06-08 cs.CV

Qinhong Yang , Dongdong Chen , Zhentao Tan , Qiankun Liu , Qi Chu , Jianmin Bao , Lu Yuan , Gang Hua
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Nenghai Yu
This is my paper · ORCID
classification cs.CV
keywords hq-50krestorationdatasetimageaspectsdamoedatadatasets
verification ladder T0 review T1 audit T2 compute T3 formal
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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}.

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Cited by 4 Pith papers

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