Research and Implementation of Image Defogging Based on Deep Learning

Image degradation due to fog and haze is a common problem in the field of life and image research. This article will be designed and implemented of image haze removal algorithm. The traditional methods have some problems such as low brightness, artifact, halo and distortion, but the existing methods based on deep learning have better effect. In this paper, we will propose a de-fogging network model, which is based on adversarial network. The generator in adversarial network is a convolutional neural network with residual network, which reduces the model training parameters and improves the parameter utilization. The discriminator uses the full convolutional network as the discriminator, which enhances the spatial sensitivity of the model to the image. In order to improve efficiency, the discriminator is simplified on the original structure. The loss function of the model is improved so that the model can get better training effect.