Channel attention based wavelet cascaded network for image super-resolution

被引：0
作者：
Chen J. [1 ]
Huang D. [1 ]
Huang W. [2 ]
机构：
[1] College of Engineering, Huaqiao University, Quanzhou
[2] School of Information Science and Technology, Xiamen University Tan Kah Kee College, Zhangzhou
来源：
High Technology Letters | 2022年 / 28卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Convolutional neural network(CNN); Image super-resolution (SR); Non-local self-similarity; Second-order channel attention (SOCA); Wavelet transform;
D O I：
10.3772/j.issn.1006-6748.2022.02.010
中图分类号：
学科分类号：
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. Copyright © by HIGH TECHNOLOGY LETTERS PRESS.
引用
收藏
页码：197 / 207
页数：10
相关论文
共 39 条
  • [1] ZENG K, DING S, JIA W., Single image super-resolution using a polymorphic parallel CNN, Applied Intelligence, 49, 1, pp. 292-300, (2019)
  • [2] LEI S, SHI Z, ZOU Z., Coupled adversarial training for remote sensing image super-resolution, IEEE Transactions on Geoscience and Remote Sensing, 58, 5, pp. 3633-3643, (2020)
  • [3] ZHU J, ZENG H, HUANG J, Et al., Vehicle re-identification using quadruple directional deep learning features, IEEE Transactions on Intelligent Transportation Systems, 21, 1, pp. 410-420, (2020)
  • [4] CHEN J, CHEN J, WANG Z, Et al., Identity-aware face super-resolution for low-resolution face recognition, IEEE Signal Processing Letters, 27, pp. 645-649, (2020)
  • [5] TAI Y, YANG J, LIU X, Et al., MemNet: a persistent memory network for image restoration, Proceedings of IEEE International Conference on Computer Vision, pp. 4549-4557, (2017)
  • [6] ZHANG Y, TIAN Y, KONG Y, Et al., Residual dense network for image super-resolution, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2472-2481, (2018)
  • [7] HUANG G, LIU Z, VAN DER MAATEN L, Et al., Densely connected convolutional networks, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2261-2269, (2017)
  • [8] LIU D, WEN B, FAN Y, Et al., Non-local recurrent network for image restoration, Proceedings of Advances in Neural Information Processing Systems, pp. 1680-1689, (2018)
  • [9] HU J, SHEN L, ALBANIE S, Et al., Squeeze-and-excitation networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 8, pp. 2011-2023, (2020)
  • [10] ZHANG Y, LI K, LI K, Et al., Image super-resolution using very deep residual channel attention networks, Proceedings of European Conference on Computer Vision, pp. 294-310, (2018)
1234