Image super-resolution network based on multi-scale adaptive attention

被引：1
|
作者：
Zhou Y. [1 ,2 ]
Pei S. [1 ]
Chen H. [1 ,2 ]
Xu S. [1 ]
机构：
[1] School of Artificial Intelligence, Hebei University of Technology, Tianjin
[2] China Hebei Control Engineering Research Center, Tianjin
来源：
Guangxue Jingmi Gongcheng/Optics and Precision Engineering | 2024年 / 32卷 / 06期
关键词：
adaptive weights; attention mechanism; multi-scale feature; progressive information interaction; super-resolution;
D O I：
10.37188/OPE.20243206.0843
中图分类号：
学科分类号：
摘要：
Aiming at the problem that most image super-resolution methods cannot fully extract features by using single-scale convolution，an image super-resolution network based on multi-scale adaptive atten⁃ tion is proposed. To fully use the contextual information in each hierarchical feature，a multi-scale feature fusion block was designed，whose basic unit consists of an adaptive dual-scale block，a multi-path progres⁃ sive interactive block，and an adaptive dual-dimensional attention sequentially in series. Firstly，the adap⁃ tive dual-scale block autonomously fused the features of two scales to obtain richer contextual features；sec⁃ ondly，the multi-path progressive interactive block interacted the output of the adaptive dual-scale block in a progressive way to improve the correlation between the contextual features；lastly，the adaptive dual-di⁃ mensional attention autonomously selected different dimensions of the attention to refine the output fea⁃ tures，which makes the output features more discriminative. The experimental results show that on Set5，Set14，BSD100 and Urban100 test sets，the method of this paper improves the PSNR and SSIM quantita⁃ tive metrics compared to other mainstream methods，especially for the Urban100 test set，where texture details are difficult to be recovered，the method of this paper improves PSNR and SSIM metrics by 0. 05 dB and 0. 004 5 respectively compared to the existing optimal method，SwinIR，with the scaling fac⁃ tor of ×4；in terms of visual effect，the reconstructed images in this paper have more texture details. © 2024 Chinese Academy of Sciences. All rights reserved.
引用
收藏
页码：843 / 856
页数：13
相关论文
共 33 条
  • [21] HUANG J B，, SINGH A, AHUJA N., Single image super-resolution from transformed self-exemplars［C］, 2015 IEEE Conference on Computer Vision and Pattern Recognition （CVPR）, pp. 5197-5206, (2015)
  • [22] WANG Y J, Et al., Image quality evaluation based on image weighted separating block peak signal to noise ratio［C］, International Conference on Neural Networks and Signal Processing，2003. Proceedings of the 2003, pp. 994-997
  • [23] WANG Z, SHEIKH H R，, Et al., Image quality assessment：from error visibility to structural similarity［J］, IEEE Transactions on Image Processing, 13, 4, pp. 600-612, (2004)
  • [24] KINGMA DP，BA J L. Adam：a method for stochastic optimization ［J］, (2014)
  • [25] WANG Q L, ZHU P F，, Et al., ECA-Net：efficient channel attention for deep convolutional neural networks［C］, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition（CVPR）, pp. 11531-11539, (2020)
  • [26] WOO S H, LEE J Y，, Et al., CBAM：convolutional block attention module［C］, Proceedings of the 15th European Conference on Computer Vision （ECCV）, pp. 3-19, (2018)
  • [27] WANG F Y, SHEN C., BAM：A Lightweight and Efficient Balanced Attention Mechanism for Single Image Super Resolution［EB/ OL］, (2021)
  • [28] WANG X H, WANG Q, ZHAO Y Z，, Et al., Lightweight Single-Image Super-Resolution Network with Attentive Auxiliary Feature Learning ［M］, Computer Vision-ACCV 2020, pp. 268-285, (2021)
  • [29] ZHANG Y L, TIAN Y P，, KONG Y，, Et al., Residual dense network for image super-resolution ［C］, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2472-2481, (2018)
  • [30] LEI P C., An efficient group skip-connecting network for image super-resolution［J］, Knowledge-Based Systems, 222, (2021)
1234