Detection of White Gaussian Noise and Its Degree in Image Processing Using Generative Adversarial Nets

  • Wentao Hua
  • Jian XiongEmail author
  • Jie Yang
  • Guan Gui
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)


Since the theory of generative adversarial nets (GANs) put forward in 2014, various applications based on GANs have been developed. Most of the applications focused on generator network (G) of GANs to solve the daily challenges. However, rare of them had been aware of the great value of the discriminator network (D). In this paper, we propose a new method of detecting white Gaussian noise and its degree by the discriminator of generative adversarial nets. The results of our experiments show the feasibility of detecting white Gaussian noise (WGN) and evaluating its degree through generative adversarial nets.


Generative adversarial nets Gaussian white noise detection Deep learning 


  1. 1.
    Tomasi C, Manduchi R. Bilateral filtering for gray and color images. In: Sixth international conference computer vision (IEEE Cat. No.98CH36271). p. 839–46.Google Scholar
  2. 2.
    Buades A, Coll B, Morel J-M. A non-local algorithm for image denoising. In: IEEE computer society conference computer vision pattern recognition, 2005. CVPR 2005, vol. 2; 2005. p. 60–5.Google Scholar
  3. 3.
    Dabov K, Foi A, Katkovnik V. Image denoising by sparse 3D transformation-domain collaborative filtering. IEEE Trans Image Process. 2007;16(8):1–16.MathSciNetCrossRefGoogle Scholar
  4. 4.
    Chuah JH, Khaw HY, Soon FC, Chow C. Detection of Gaussian noise and its level using deep convolutional neural network; 2017. p. 2447–50.Google Scholar
  5. 5.
    Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. 2012;25:1–9.Google Scholar
  6. 6.
    Heidemann G. Unsupervised Learning.pdf. Image Vis Comput. 2005;23:861–76.CrossRefGoogle Scholar
  7. 7.
    B. N. Transform and B. N. Transform, “Batch Normalizing”.Google Scholar
  8. 8.
    Hara K, Saito D, Shouno H. Analysis of function of rectified linear unit used in deep learning. In: Proceedings of international joint conference neural networks, vol. 2015–Sept 2015.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.College of Telecommunication and Information EngineeringNanjing University of Posts and TelecommunicationsNanjingChina

Personalised recommendations