Method for Removing Motion Blur from Images of Harmful Biological Organisms in Power Places Based on Improved Cyclegan

Nowadays, the automatic detection of harmful organisms in power places has attracted attention due to the extensive unattended way of power places. However, surveillance pictures are prone to motion blurring and harmful organisms cannot be effectively detected due to their frequent and fast movements in power places. On the basis of the improved Cycle-Consistent Adversarial Networks (CycleGAN) model, we propose a method for removing motion blur from the images of harmful biological organisms in power places. This method does not require paired blurred and real sharp images for training, which is consistent with actual requirements. In addition, our method improves the classical CycleGAN model by combining cycle consistency and perceptual loss to enhance the detail authenticity of image texture restoration and improve the detection accuracy. The model uses Wasserstein GAN with gradient penalty (WGAN-GP) as a loss function to train the depth model. Given the existence of the GAN itself, the entire real image distribution space is difficult to fill with the generated image distribution space. Experimental results show that the proposed method effectively improves the detection accuracy of harmful organisms in power places.