| CHEN Jian(陈健)*,HUANG Detian*,HUANG Weiqin**.[J].高技术通讯(英文),2022,28(2):197~207 |
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| Channel attention based wavelet cascaded network for image super-resolution |
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| DOI：10.3772/j.issn.1006-6748.2022.02.010 |
| 中文关键词: |
| 英文关键词: image super-resolution (SR), wavelet transform, convolutional neural network(CNN), second-order channel attention (SOCA), non-local self-similarity |
| 基金项目: |
| Author Name | Affiliation | | CHEN Jian(陈健)* | (*College of Engineering, Huaqiao University, Quanzhou 362021, P.R.China)
(**School of Information Science and Technology, Xiamen University Tan Kah Kee College, Zhangzhou 363105, P.R.China) | | HUANG Detian* | (*College of Engineering, Huaqiao University, Quanzhou 362021, P.R.China)
(**School of Information Science and Technology, Xiamen University Tan Kah Kee College, Zhangzhou 363105, P.R.China) | | HUANG Weiqin** | (*College of Engineering, Huaqiao University, Quanzhou 362021, P.R.China)
(**School of Information Science and Technology, Xiamen University Tan Kah Kee College, Zhangzhou 363105, P.R.China) |
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| 中文摘要: |
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| 英文摘要: |
| Convolutional neural networks (CNNs) have shown great potential for image super-resolution (SR). However, most existing CNNs only reconstruct images in the spatial domain, resulting in insufficient high-frequency details of reconstructed images. To address this issue, a channel attention based wavelet cascaded network for image super-resolution (CWSR) is proposed. Specifically, a second-order channel attention (SOCA) mechanism is incorporated into the network, and the covariance matrix normalization is utilized to explore interdependencies between channel-wise features. Then, to boost the quality of residual features, the non-local module is adopted to further improve the global information integration ability of the network. Finally, taking the image loss in the spatial and wavelet domains into account, a dual-constrained loss function is proposed to optimize the network. Experimental results illustrate that CWSR outperforms several state-of-the-art methods in terms of both visual quality and quantitative metrics. |
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