In this paper, we present Uformer, an effective and efficient Transformer-based architecture for image restoration, in which we build a hierarchical encoder-decoder network using the Transformer block. Without bells and whistles, our Uformer achieves superior or comparable performance compared with the advanced algorithms.
This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.
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| Category | 🤖 Artificial Intelligence |
| Published | Jun 01, 2022 |
| Journal | 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
| Authors | Zhendong Wang, Xiaodong Cun, Jianmin Bao, Wengang Zhou, Jianzhuang Liu |
| DOI | 10.1109/cvpr52688.2022.01716 |
| Citations | 2,002 |
| Source | OpenAlex |