The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
Activating More Pixels in Image Super-Resolution Transformer
Open accessAbstract
Transformer-based methods have shown impressive performance in low-level vision tasks, such as image super-resolution. However, we find that these networks can only utilize a limited spatial range of input information through attribution analysis. This implies that the potential of Transformer is still not fully exploited in existing networks. In order to activate more input pixels for better reconstruction, we propose a novel Hybrid Attention Transformer (HAT). It combines both channel attention and window-based self-attention schemes, thus making use of their complementary advantages of being able to utilize global statistics and strong local fitting capability. Moreover, to better aggregate the cross-window information, we introduce an overlapping cross-attention module to enhance the interaction between neighboring window features. In the training stage, we additionally adopt a same-task pre-training strategy to exploit the potential of the model for further improvement. Extensive experiments show the effectiveness of the proposed modules, and we further scale up the model to demonstrate that the performance of this task can be greatly improved. Our overall method significantly outperforms the state-of-the-art methods by more than 1dB.
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End-to-End Object Detection with Transformers
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Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
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Self-citationMasked Autoencoders Are Scalable Vision Learners
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“ods, numerous deep networks [10,11,45,19,23,30,67,65,7,41,40,29,26] have been proposed for SR to further improve the reconstruction quality.”
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Advances in medical image analysis with vision Transformers: A comprehensive review
Azad, R., Kazerouni, A., Heidari, M., Aghdam, E. K., Molaei, A., Jia, Y., Jose, A., Roy, R., & Merhof, D. (2024) · Medical Image Analysis · doi:10.1016/j.media.2023.103000 · Cited by 485
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DiffBIR: Toward Blind Image Restoration with Generative Diffusion Prior
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Self-citationDiffusion Models, Image Super-Resolution, and Everything: A Survey
Moser, B. B., Shanbhag, A. S., Raue, F., Frolov, S., Palacio, S., & Dengel, A. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3476671 · Cited by 117
NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results
Li, Y., Zhang, Y., Timofte, R., Van Gool, L., Yu, L., Li, Y., Li, X., Jiang, T., Wu, Q., Han, M., et al. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw59228.2023.00189 · Cited by 72
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Metadata
- Authors
- Chen, X., Wang, X., Zhou, J., Qiao, Y., & Dong, C.
- Venue
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2023-06
- Pages
- 22367-22377
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4386083034
- Cited by
- 1,152
- References
- 65
- FWCI
- 130.08
- Type
- conference paper
- Language
- en
- Open access
- green
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- Chen, X., Wang, X., Zhou, J., Qiao, Y., & Dong, C. (2023). Activating More Pixels in Image Super-Resolution Transformer. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr52729.2023.02142
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