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Improving Single-Image Super-Resolution with Dilated Attention.

Zhang, X., Cheng, B., Yang, x., Xiao, Z., Zhang, J. J. and You, L., 2024. Improving Single-Image Super-Resolution with Dilated Attention. Electronics, 13 (12), 2281.

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electronics-13-02281.pdf - Published Version
Available under License Creative Commons Attribution.


DOI: 10.3390/electronics13122281


Single-image super-resolution (SISR) techniques have become a vital tool for improving image quality and clarity in the rapidly evolving field of digital imaging. Convolutional neural network (CNN) and transformer-based SISR techniques are very popular. However, CNN-based techniques are not suitable when capturing long-range dependencies, and transformer-based techniques suffer from computational complexity. To tackle these problems, this paper proposes a novel method called dilated attention-based single-image super-resolution (DAIR). It comprises three components: low-level feature extraction, multi-scale dilated transformer block (MDTB), and high-quality image reconstruction. A convolutional layer is used to extract the base features from low-resolution images, which lays the foundation for subsequent processing. Dilated attention is introduced to MDTB to enhance its ability to capture image features at different scales and ensure superior image details and structure recovery. After that, MDTB refines these features to extract multi-scale global attributes and effectively grasps images’ long-distance relationships and features across multiple scales. Finally, low-level features obtained from feature extraction and multi-scale global features obtained from MDTB are aggregated to reconstruct high-resolution images. The comparison with existing methods validates the efficacy of the proposed method and demonstrates its advantage in improving image resolution and quality.

Item Type:Article
Uncontrolled Keywords:single-image super-resolution; dilated attention; feature extraction; multi-scale dilated transformer block; image reconstruction
Group:Faculty of Media & Communication
ID Code:39977
Deposited By: Symplectic RT2
Deposited On:12 Jun 2024 13:39
Last Modified:12 Jun 2024 13:39


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