A GAN-based Background Noise Removal Method on Infrared Image of Gas-Insulated Transmission Line
Shan Gao, Hongtao Li, Ke Zhao, Yujie Li, Yongfei Liu, Jingtan Ma · 2021
Results of infrared inspection on 1100kV pipe gallery gas-insulated transmission line (GIL) project are seriously interfered by background noise such as the LED lights and induced heating on steel structures. In this paper, an image background noise removal method based on generative adversarial network (GAN) is proposed. Firstly, convolution neural network (CNN) is used to classify the different parts of GIL. Secondly, the method of threshold and graying are used to mark the classified parts. Finally, the general adverse network is used to repair marked interference parts of noise. In which the generator of GAN is used to repair the marked region to generate new infrared images without noise, and the discriminator is used to discriminate whether the new image output by the generator is successfully repaired. The results show that the proposed method can achieve better background removal effect on infrared images, and the texture feature of the image can preserve well when using GAN to remove the image noise compared with the image de-noising method of VAE. The application results on site show that it takes 0.26 seconds to classify each infrared image using CNN and 4 seconds to remove noise using GAN.