Computer Science > Computer Vision and Pattern Recognition
arXiv:2306.05390 (cs)
[Submitted on 8 Jun 2023]
Title:HQ-50K: A Large-scale, High-quality Dataset for Image Restoration
Authors:Qinhong Yang,Dongdong Chen,Zhentao Tan,Qiankun Liu,Qi Chu,Jianmin Bao,Lu Yuan,Gang Hua,Nenghai Yu
View a PDF of the paper titled HQ-50K: A Large-scale, High-quality Dataset for Image Restoration, by Qinhong Yang and Dongdong Chen and Zhentao Tan and Qiankun Liu and Qi Chu and Jianmin Bao and Lu Yuan and Gang Hua and Nenghai Yu
View PDFAbstract: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{this https URL}.
| Comments: | Dataset and code will be available atthis https URL |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2306.05390 [cs.CV] |
| (orarXiv:2306.05390v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2306.05390 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled HQ-50K: A Large-scale, High-quality Dataset for Image Restoration, by Qinhong Yang and Dongdong Chen and Zhentao Tan and Qiankun Liu and Qi Chu and Jianmin Bao and Lu Yuan and Gang Hua and Nenghai Yu
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