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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)

Abstract

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.

Keywords

Generative adversarial nets Gaussian white noise detection Deep learning 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

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

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