Image Inpainting based on Gated Convolution and spectral Normalization

Abstract

Traditional image inpainting methods based on deep learning have the problem of insufficient discrimination between the missing area and the global area information in the feature extraction of image inpainting tasks because of the characteristics of the model constructed by the convolutional layer. At the same time, the traditional generative adversarial network often has problems such as training difficulties and model collapse in the training process. To solve the above problems and improve the repair effect of the model, this paper proposes a dual discriminator image inpainting model based on generative adversarial network combining gated convolution and spectral normalization. The model is mainly composed of an image inpainting module and an image recognition module. The traditional image inpainting model considers all input pixels as valid pixels when extracting the features of the image to be inpainted, which is unreasonable for the image inpainting task. In order to solve this problem, the gated convolution is designed to replace the role of traditional convolution in the image inpainting module. Gated convolutions address the irrationality of ordinary convolutions in image inpainting tasks by providing learnable dynamic feature selection mechanisms for each channel at each spatial location in all layers. At the same time, generative image inpainting models usually have problems such as training hard mode collapse during the training process. In this paper, we intend to introduce the spectral normalization mechanism in the convolutional layer design of the discriminator module. By introducing Lipschitz continuity constraints from the spectral norm of the parameter matrix of each layer of the neural network, the neural network is better insensitive to input perturbations, making the training process more stable and easier to converge. It solves the problems of mode collapse and model training difficulty in the training of image inpainting model based on generative adversarial network. Finally, qualitative and quantitative experiments show that the image inpainting model based on gated convolution and spectral normalization solves the above problems, and the inpainted image has reasonable texture structure and contextual semantic information.