In order to improve the dehazing effect of foggy images, an image defogging algorithm based on conditional generation against network was proposed. Through end-to-end trainable nerves, the network trained the synthesized indoor and outdoor data sets. In order to capture more useful information in the image, the generator and discriminator architecture was designed in the generation network, the loss function was modified using the pre-trained visual geometry group feature model and the L1-regular gradient pair loss. At the last level of the discriminator, the Sigmoid function was applied to the feature map for probabilistic analysis to be normalized. By using the synthetic data set to train the loss function, the parameters of the new loss function were obtained, and then the new trained loss function was tested by the outdoor natural fog image data set. The experimental results show that the algorithm effectively solves the problem of color distortion, oversaturated, and visual artifacts, resulting in a better defogging image.