Abstract:Designing and utilizing good image prior knowledge is an important way to enable image inpainting. A generative adversarial network (GAN) is an excellent generative model, and its generator can learn rich image semantic information from large datasets. Thus, it is a good choice to use a pre-trained GAN model as an image prior. Making use of multiple hidden variables, this study adds adaptive weights to the channels and feature maps at the same time in the middle layer of the pre-trained generator and fine-tunes generator parameters in the training process. In this way, the pre-trained GAN model can be used for better image inpainting. Finally, through the contrast experiment of image reconstruction and image inpainting and the combination of qualitative and quantitative analysis, the proposed method is proved effective to mine the prior knowledge of the pre-trained model, thus finishing the task of image inpainting with high quality.