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Uformer: A General U-Shaped Transformer for Image Restoration

📅 June 1, 2022 👤 Zhendong Wang, Xiaodong Cun, Jianmin Bao et al. 📖 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 📊 2,002 citations

🤖 Plain-English Summary

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.

🔑 Key Findings

  • In Uformer, there are two core designs.
  • First, we introduce a novel locally-enhanced window (LeWin) Transformer block, which performs non-overlapping window-based self-attention instead of global self-attention.
  • It significantly reduces the computational complexity on high resolution feature map while capturing local context.

💡 Why This Matters

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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📋 Article Details

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

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