Username   Password       Forgot your password?  Forgot your username? 

 

Remote Sensing Image Super-Resolution Reconstruction based on Generative Adversarial Network

Volume 15, Number 7, July 2019, pp. 1783-1791
DOI: 10.23940/ijpe.19.07.p4.17831791

Aili Wanga, Ying Wanga, Xiaoying Songa, and Yuji Iwahorib

aHigher Education Key Lab for Measuring and Control Technology and Instrumentations of Heilongjiang
Harbin University of Science and Technology, Harbin, 150001, China
bComputer Science, Chubu University, Aichi, 487-8501, Japan

 

(Submitted on December 10, 2018; Revised on January 12, 2019; Accepted on February 10, 2019)

Abstract:

The super-resolution reconstruction algorithm based on generative adversarial network (GAN) can generate realistic texture in the super-resolution process of a single remote sensing image. In order to further improve the visual quality of the reconstructed image, this paper will improve the generation network, discrimination network, and perceptual loss of the generated confrontation network. Firstly, the batch normalization layer is removed and dense connections are used in the residual blocks, which effectively improves the performance of the generated network. Then, we use the relative discriminant network to learn more detailed texture. Finally, we obtain the perception loss before the activation function to maintain the consistency of brightness. In addition, transfer learning is used to solve the problem of insufficient remote sensing data. The experimental results show that the proposed algorithm has superiority in the super-resolution reconstruction of remote sensing images and can obtain better subjective visual effects.

 

References: 16

  1. Y. Y. Zhang, W. Wu, Y. Dai, X. M. Yang, B. Y. Yan, and W. Lu, “Remote Sensing Images Super-Resolution based on Sparse Dictionaries and Residual Dictionaries,” in Proceedings of IEEE International Conference on Dependable, Autonomic and Secure Computing, pp. 318-323, 2013
  2. W. Wu, X. M. Yang, K. Liu, Y. G. Liu, B. Y. Yan, and H. Hua, “A New Framework for Remote Sensing Image Super-Resolution: Sparse Representation-based Method by Processing Dictionaries with Multi-Type Features,” Journal of Systems Architecture, Vol. 64, pp. 63-75, 2016
  3. C. Dong, C. L. Chen, K. M. He, and X. O. Tang, “Learning a Deep Convolutional Network for Image Super-Resolution,” in Proceedings of European Conference on Computer Vision, pp. 184-199, 2014
  4. C. Dong, C. C. Loy, K. He, and X. O. Tang, “Image Super-Resolution using Deep Convolutional Networks,” IEEE Transactions on Pattern Analysis & Machine Intelligence, Vol. 38, No. 2, pp. 295-307, 2016
  5. K. Simonyan and A. Zissermanet, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” in Proceedings of International Conference on Learning Representations, pp. 473-491, 2015
  6. K. M. He, X. Y. Zhang, S. Q. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016
  7. J. Kim, J. K. Lee, and K. M. Lee, “Accurate Image Super-Resolution using Very Deep Convolutional Networks,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646-1654, 2016
  8. J. Kim, J. K. Lee, and K. M. Lee, “Deeply-Recursive Convolutional Network for Image Super-Resolution,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1637-1645, 2016
  9. J. Johnson, A. Alahi, and F. F. Li, “Perceptual Losses for Real-Time Style Transfer and Super-Resolution,” in Proceedings of European Conference on Computer Vision, pp. 1-18, 2016
  10. J. Bruna, P. Sprechmann, and Y. L. Cun, “Super-Resolution with Deep Convolutional Sufficient Statistics,” in Proceedings of International Conference of Learning Representation, pp. 2-6, 2015
  11. I. J. Goodfellow, J. P. Abadie, and M. Mirza, “Generative Adversarial Networks,” in Proceedings of Conference and Workshop on Neural Information Processing Systems, pp. 4-7, 2014
  12. C. Ledig, L. Theis, and F. Huszar, “Photo-Realistic Single Image Super-Resolution using a Generative Adversarial Network,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 105-114, 2017
  13. S. M. Mehdi, B. Schölkopf, and M. Hirsch, “EnhanceNet: Single Image Super-Resolution through Automated Texture Synthesis,” in Proceedings of IEEE International Conference on Computer Vision, pp. 4501- 4510, 2017
  14. K. M. He, X. Y. Zhang, and S. Q. Re, “Deep Residual Learning for Image Recognition,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778, 2016
  15. B. Lim and S. Son, “Enhanced Deep Residual Networks for Single Image Super-Resolution,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1132-1140, 2017
  16. J. Xu, Y. Chae, and B. Stenger, “Residual Dense Network for Image Super Resolution,” in Proceedings of IEEE International Conference on Image Processing, pp. 71-75, 2018

 

Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

 
This site uses encryption for transmitting your passwords. ratmilwebsolutions.com