Computer Science and Information Engineering

Weihong Shen This email address is being protected from spambots. You need JavaScript enabled to view it.1

1Art and Sports Department, Henan Technical College of Construction, Zhengzhou 450064 China

 

Received: July 17, 2022
Accepted: August 8, 2022
Publication Date: September 15, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202306_26(6).0009  


ABSTRACT

Aiming at the problem that the noise in the process of image acquisition and transmission leads to the degradation of the subsequent image processing capability, we propose a conditional generative adversarial network (CGAN) based on graph attention network for moving image denoising. The CGAN can effectively extract depth features and avoid losing details. The discriminant network is constructed based on fully convolutional network, so pixel classification can be obtained to improve the accuracy of discrimination. In addition, in order to improve the denoising ability and preserve the image details as much as possible, a graph attention network is proposed. The compound loss function is constructed based on confrontation loss, visual perception loss and mean square error loss. Finally, the adaptive weighted average is used to fuse the three-channel output information to obtain the final denoised image. Experimental results show that compared with other state-of-the-art denoising algorithms, the proposed algorithm can effectively remove image noise and restore original image details.


Keywords: CGAN; Graph attention network; Moving image denoising; Adaptive weighted average


REFERENCES

  1. [1] H. Zeng, X. Xie, and J. Ning, (2021) “Hyperspectral image denoising via global spatial-spectral total variation regularized nonconvex local low-rank tensor approximation" Signal Processing 178: 107805.
  2. [2] J.-C. Cheng, C. Bevington, A. Rahmim, I. Klyuzhin, J. Matthews, R. Boellaard, and V. Sossi, (2021) “Dynamic PET image reconstruction utilizing intrinsic datadriven HYPR4D denoising kernel" Medical Physics 48(5): 2230–2244.
  3. [3] S. Ganesan, K. Dhanasekaran, and N. Nishavithri. “De-noising The Image Using Non Local Means Filtering”. In: 2021 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE. 2021, 1–5.
  4. [4] J. H. Friedman and J.W. Tukey, (1974) “A projection pursuit algorithm for exploratory data analysis" IEEE Transactions on computers 100(9): 881–890.
  5. [5] S. Shrestha, (2014) “Image denoising using new adaptive based median filters" arXiv preprint arXiv:1410.2175:
  6. [6] K. He, X. Zhang, S. Ren, and J. Sun. “Deep residual learning for image recognition”. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016, 770–778.
  7. [7] J. G. Serra, M. Testa, R. Molina, and A. K. Katsaggelos, (2017) “Bayesian K-SVD using fast variational inference" IEEE Transactions on Image Processing 26(7): 3344–3359.
  8. [8] K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, (2007) “Image denoising by sparse 3-D transform-domain collaborative filtering" IEEE Transactions on image processing 16(8): 2080–2095.
  9. [9] L. Teng and H. Li, (2019) “CSDK: A Chi-square distribution-Kernel method for image de-noising under the Internet of things big data environment" International Journal of Distributed Sensor Networks 15(5): 1550147719847133.
  10. [10] X. Wang, Q. Tao, L. Wang, D. Li, and M. Zhang. “Deep convolutional architecture for natural image denoising”. In: 2015 International Conference on Wireless Communications & Signal Processing (WCSP). IEEE.2015, 1–4.
  11. [11] K. Zhang,W. Zuo, Y. Chen, D. Meng, and L. Zhang, (2017) “Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising" IEEE transactions on image processing 26(7): 3142–3155.
  12. [12] S. U. Khan, I. U. Khan, I. Ullah, N. Saif, and I. Ullah, (2020) “A review of airport dual energy X-ray baggage inspection techniques: image enhancement and noise reduction" Journal of X-ray Science and Technology 28(3): 481–505.
  13. [13] O. Ronneberger, P. Fischer, and T. Brox. “U-net: Convolutional networks for biomedical image segmentation”. In: International Conference on Medical image computing and computer-assisted intervention. Springer. 2015, 234–241.
  14. [14] M. Ran, J. Hu, Y. Chen, H. Chen, H. Sun, J. Zhou, and Y. Zhang, (2019) “Denoising of 3D magnetic resonance images using a residual encoder–decoder Wasserstein generative adversarial network" Medical image analysis 55: 165–180.
  15. [15] N. Divakar and R. Venkatesh Babu. “Image denoising via CNNs: An adversarial approach”. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. 2017, 80–87.
  16. [16] J. M. Wolterink, T. Leiner, M. A. Viergever, and I. Išgum, (2017) “Generative adversarial networks for noise reduction in low-dose CT" IEEE transactions on medical imaging 36(12): 2536–2545.
  17. [17] Q. Yang, P. Yan, Y. Zhang, H. Yu, Y. Shi, X. Mou, M. K. Kalra, Y. Zhang, L. Sun, and G. Wang, (2018) “Lowdose CT image denoising using a generative adversarial network with Wasserstein distance and perceptual loss" IEEE transactions on medical imaging 37(6): 1348–1357.
  18. [18] H. S. Park, J. Baek, S. K. You, J. K. Choi, and J. K. Seo, (2019) “Unpaired image denoising using a generative adversarial network in X-ray CT" IEEE Access 7: 110414–110425.
  19. [19] R. Rajeev, J. A. Samath, and N. Karthikeyan, (2019) “An intelligent recurrent neural network with long short-term memory (LSTM) BASED batch normalization for medical image denoising" Journal of medical systems 43(8): 1–10.
  20. [20] P. C. Tripathi and S. Bag, (2020) “CNN-DMRI: a convolutional neural network for denoising of magnetic resonance images" Pattern Recognition Letters 135: 57–63.
  21. [21] S. Chen, D. Shi, M. Sadiq, and X. Cheng, (2020) “Image denoising with generative adversarial networks and its application to cell image enhancement" IEEE Access 8: 82819–82831.
  22. [22] L. Wang, Y. Shoulin, H. Alyami, A. A. Laghari, M. Rashid, J. Almotiri, H. J. Alyamani, and F. Alturise. A novel deep learning-based single shot multibox detector model for object detection in optical remote sensing images. 2022.
  23. [23] X.Wang, S. Yin, M. Shafiq, A. A. Laghari, S. Karim, O. Cheikhrouhou, W. Alhakami, and H. Hamam, (2022) “A new V-net convolutional neural network based on four-dimensional hyperchaotic system for medical image encryption" Security and Communication Networks 2022:
  24. [24] A. Jisi, S. Yin, et al., (2021) “A new feature fusion network for student behavior recognition in education" Journal of Applied Science and Engineering 24(2): 133–140.
  25. [25] M. Sandilya, S. Nirmala, and N. Saikia, (2021) “Compressed Sensing MRI Reconstruction Using Generative Adversarial Network with Rician De-noising" Applied Magnetic Resonance 52(11): 1635–1656.
  26. [26] J. Akilandeswari, G. Jothi, A. Naveenkumar, R. Sabeenian, P. Iyyanar, and M. Paramasivam, (2022) “Design and development of an indoor navigation system using denoising autoencoder based convolutional neural network for visually impaired people" Multimedia Tools and Applications 81(3): 3483–3514.
  27. [27] H. Ji, C. Liu, Z. Shen, and Y. Xu. “Robust video denoising using low rank matrix completion”. In: 2010 IEEE computer society conference on computer vision and pattern recognition. IEEE. 2010, 1791–1798.
  28. [28] K. Ma, K. Zeng, and Z. Wang, (2015) “Perceptual quality assessment for multi-exposure image fusion" IEEE Transactions on Image Processing 24(11): 3345–3356.
  29. [29] C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, et al. “Photo-realistic single image superresolution using a generative adversarial network”. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2017, 4681–4690.
  30. [30] R.Wang, X. Xiao, B. Guo, Q. Qin, and R. Chen, (2018) “An effective image denoising method for UAV images via improved generative adversarial networks" Sensors 18(7): 1985.
  31. [31] S. Lee, Y. Chung, C. Kim, R. Shrestha, and W. Kim, (2022) “Thermographic Inspection of CLP Defects on the Subsurface Based on Binary Image" International Journal of Precision Engineering and Manufacturing 23(3): 269–279.
  32. [32] R. M. Garcıa, A. Kharitonenkov, O. Kuzmin, and V. Snezhko, (2021) “Determining the tribological features of concrete and assessing the service life of architectural facilities" Journal of Measurements in Engineering 9(4): 207–217.
  33. [33] M. Ding, Z. Shi, B. Du, H.Wang, and L. Han, (2021) “A signal de-noising method for a MEMS gyroscope based on improved VMD-WTD" Measurement Science and Technology 32(9): 095112.

Latest Articles

Dynamic Analysis of the UHPFRC High-rise Building under High Explosion using Coupled Eulerian-Lagrangian Approach
Dynamic Analysis of the UHPFRC High-rise Building under High Explosion using Coupled Eulerian-Lagrangian ApproachVolume 28, Issue 2 (In Press)

Read more: ...

NSGA-II Algorithm-Based Wide Area Measurement (WAMS) Allocation with Considering Reliability
NSGA-II Algorithm-Based Wide Area Measurement (WAMS) Allocation with Considering ReliabilityVolume 28, Issue 2 (In Press)

Read more: ...

Predicting Demand for Emergency Ambulance Services: A Comparative Approach
Predicting Demand for Emergency Ambulance Services: A Comparative ApproachVolume 27, Issue 10

Read more: ...

Multi-level Semantic Fusion Network for Few-shot Multimedia Image Recognition in Education Management
Multi-level Semantic Fusion Network for Few-shot Multimedia Image Recognition in Education ManagementVolume 28, Issue 2 (In Press)

Read more: ...

River bank erosion prediction using multivariable linear regression
River bank erosion prediction using multivariable linear regressionVolume 27, Issue 12 (In Press)

Read more: ...

Optimization of Ultrasound-Assisted Pectin Extraction from Durian Rind
Optimization of Ultrasound-Assisted Pectin Extraction from Durian RindVolume 28, Issue 2 (In Press)

Read more: ...

Short-time prediction model for cross-section passenger flow in urban transit trains using GCN-AM-BiLSTM
Short-time prediction model for cross-section passenger flow in urban transit trains using GCN-AM-BiLSTMVolume 28, Issue 2 (In Press)

Read more: ...

Response Prediction Analysis of RC Frame Structures Using Support Vector Machine Algorithm
Response Prediction Analysis of RC Frame Structures Using Support Vector Machine AlgorithmVolume 28, Issue 2 (In Press)

Read more: ...

Using the non-dominated sorting genetic algorithm II for multi-objective optimization of acoustic performance in sandwich panels with cellular honeycomb core
Using the non-dominated sorting genetic algorithm II for multi-objective optimization of acoustic performance in sandwich panels with cellular honeycomb coreVolume 28, Issue 2 (In Press)

Read more: ...