Gray image super-resolution reconstruction based on improved RDN method

被引：0
|
作者：
Wei Z. [1 ]
Liu Y. [1 ]
机构：
[1] School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun
来源：
Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering | 2020年 / 49卷
关键词：
Convolution residual network; Gray image; Super-resolution reconstruction;
D O I：
10.3788/IRLA20200173
中图分类号：
学科分类号：
摘要：
Aiming at the problem of residual network super-resolution reconstruction by residual algorithm, an improved deep composite residual network model for residual calculation was proposed. In this research, the original residual block was improved, which could make full use of all the convolutional layer feature information inside the residual block to improve the quality of the generated image; a double-layer composite structure was set to deepen the depth of the model structure, it could enhance the feature extraction of the image by the model and could extract more image features; the image feature information was enhanced using the method of transfer learning through transfer learning in the deep network structure, making the performance of the model more stable. The application experiment of the Tiangong -1 grayscale image show that the improved deep residual dense network proposed in this study performs well in the Tiangong-1 grayscale image super-resolution reconstruction, and has application value and research significance in the field of satellite imagery. © 2020, Editorial Board of Journal of Infrared and Laser Engineering. All right reserved.
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  • [1] Su Heng, Zhou Jie, Zhang Zhihao, Survey of super-resolution image reconstruction methods, Acta Automatica Sinica, 39, 8, pp. 1202-1213, (2013)
  • [2] Liu Yuefeng, Yang Hanxi, Cai Shuang, Et al., Single image super-resolution reconstruction method based on improved convolutional neural network, Journal of Computer Applications, 39, 5, pp. 1440-1447, (2019)
  • [3] Liu Na, Li Cuihua, Single image super-resolution reconstruction via deep convolutional neural network, China Sciencepaper, 10, 2, pp. 201-206, (2015)
  • [4] Lim B, Son S, Kim H, Et al., Enhanced deep residual networks for single image super-resolution, 2017 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1132-1140, (2017)
  • [5] Bevilacqua M, Roumy A, Guilletmot C, Et al., Super-resolution using neighbor embedding of back projection residuals, 18th International Conference on Digital Signal Processing, pp. 1-8, (2013)
  • [6] Huang J B, Singh A, Ahuja N., Single image super-resolution using transformed self-exemplars, 2015 IEEE Conference on Computer Vision and Pattern Recognition, pp. 5197-5206, (2015)
  • [7] Sun Xu, Li Xiaoguang, Li Jiafeng, Et al., Review on deep learning based image super-resolution restoration algorithm, Acta Automatica Sinica, 43, 5, pp. 697-709, (2017)
  • [8] Lu T, Wang J M, Zhang Y D, Et al., Satellite image super-resolution via multi-scale residual deep neural network, Remote Sensing, 11, 13, (2019)
  • [9] Khan A, Sohail A, Zahoora U, Et al., A survey of the recent architectures of deep convolutional neural Networks, (2019)
  • [10] Li Fangbiao, He Xin, Wei Zhonghui, Et al., Multiframe infrared image super-resolution reconstruction using generative adversarial networks, Infrared and Laser Engineering, 47, 2, (2018)
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