Research On Image Inpainting Technology Based On Generative Adversarial Network

Image inpainting is a method to fill the missing or occluded areas in the original image with reasonable pixel values.Traditional image inpainting methods are difficult to repair the damaged image with complex structure and strong semantic information.The generative adversarial network can generate false data through the confrontation learning and mutual optimization between the generator and the discriminator,which makes the generative adversarial network very suitable for image inpainting.Therefore,the research on image inpainting technology based on generative adversarial network is of great significance.In order to solve the problem of information loss in down sampling,hole convolution is used instead of ordinary convolution to obtain larger receptive field and reduce information loss.In order to stabilize the training of the model,the spectral normalization method is used to make the discriminator satisfy Lipschitz continuity.In order to solve the problem of poor repair details caused by insufficient semantic information acquisition in current repair methods,an improved encoder is proposed.The encoder learns region similarity from high-level semantic feature graph,and transfers the learned attention to low-level feature graph through attention transfer network to guide low-level repair and realize feature repair at different levels.In order to improve the existing image inpainting methods,such as one-time inpainting,large amount of inpainting tasks lead to incoherent results and low definition,this paper adopts the step-by-step inpainting method,and regards the whole inpainting task as the sum of several subtasks,each subtask is only responsible for a part of them,and is based on the above subtasks,and finally connected through the long short term memory Network,the overall repair results were composed.Based on the improved repair method,the repair models are tested in two datasets of CelabA and Imagenet.The experimental results show that the improved image inpainting technology can achieve the purpose of good image inpainting.Compared with the context coder and the method with context attention,the improved method achieves the best repair effect under the subjective visual evaluation and three objective evaluation indexes(peak signal-to-noise ratio,structural similarity and average absolute error).The research results of this paper can enrich the research of image inpainting technology,provide theoretical reference for the development of image inpainting technology,and have certain application value in face occlusion,cultural relic restoration,biomedical imaging and so on.