To solve the problems of traditional image rain removal methods such as image distortion and artifact generation,a generative adversarial network based single image rain removal method is proposed,which combines convolutional auto-encoding with patch penalty.Firstly,the method uses convolutional auto-encoding to form a generator network,and uses symmetric skip connections to improve the training efficiency and convergence performance of the generator network,and realizes the reconstruction of image detail information and two-dimensional signal spatial information.Secondly,PatchGAN,a Markov discriminator,is introduced to penalize on the level of image patch to remove artifacts in the generated image.Finally,a new refined loss function is proposed to participate in the training of the network model to further enhance the depth of the model's rain removal.The peak signal to noise ratio and structural similarity are taken as the evaluation criteria of the model.The experimental results show that the method has good performance in the rain removal processing of real rain images and synthetic rain images,which can elaborately restore the details of the image and ensure high visual quality.