An Improved Generative Adversarial Network for Image Dehazing

Abstract

Hazy weather degrades the quality of images obtained by computer vision related systems and affects the normal operation of the system.However, most traditional algorithms establish the relationship between the clear image model and image distortion, and most can determine the relationship between the specific image model and image distortion.The image quality obtained by the dehazing algorithm based on Convolutional Neural Network(CNN) is relatively high, but this type of algorithm has high dataset requirements and typically requires training on the data in pairs.However, it is difficult to obtain the hazy image and the haze-free image of the same scene at the same time.An improved image dehazing algorithm based on Cycle Generative Adversarial Network(CycleGAN) is proposed.The algorithm does not need to use paired data for training.By optimizing the color loss between the haze-free image generated by the generator and the real haze-free image, the generator can generate an image with the same color distribution as the haze-free image.Moreover, by adding the input of the corresponding target domain image to the two generators and introducing the feature loss function, the image distortion problem in the image conversion of classical CycleGAN is solved, improving the restoration of the detailed features of the original image.The results show that the structural similarity of the algorithm improved by 4.3%~23.0% compared with DCP, CycleGAN, AOD-Net, Cycle-dehaze, and other algorithms, and the Peak Signal-to-Noise Ratio(PSNR) improved by 2.3%~36.9%, thereby achieving an improved dehazing effect.