Research On Image Inpainting Algorithm Based On Generative Adversarial Networks

Image inpainting is a very important task that aims to inpaint a complete image from damaged or missing images.With the rapid development of generative adversarial networks(GANs),more and more models based on GANS are applied to image inpainting tasks and have made significant progress.However,existing GAN-based image inpainting methods still face some challenges.Firstly,there is a lot of invalid information in image inpainting,which can interfere with the correct feature extraction in the forward propagation of the model,causing feature bias and thus affecting the inpainted results.Secondly,convolutional neural networks(CNNs)have a problem of limited receptive fields,especially in cases where the damaged area is large,which may cause the model to not correctly perceive the image’s semantics,resulting in poor inpainted effects.In addition,the robustness of existing GAN-based methods is poor,and the inpainting effects may not meet expectations for some complex scenes or specific image types.To solve these problems,this paper explores and implements new technical solutions for image inpainting from three different directions.These techniques improve the generator structure of the GAN and are mainly applied to datasets such as places2,FFHQ,and Paris Street View,which contain landscapes,faces,and street views.This paper compares the results of experiments on different datasets and demonstrates significant improvements in performance compared to existing models.The main contributions of this paper are as follows:(1)To address the problem of invalid information generated by masks,we propose an attention normalization model suitable for image inpainting tasks,which avoids interference and data bias from invalid information.In this approach,we combine attention mechanisms and feature normalization from both spatial and channel dimensions and embed them into different positions in the network.Experiments show that this approach can help the model encode features well and produce high-quality inpainted results.(2)In order to improve global perception,we introduce the Transformer model to improve the generator structure and embed a semantic perception module into the Transformer model.The embedded semantic perception module uses an improved VQ-VAE model to learn and save high-level semantic features of images,and uses a multi-head attention mechanism to perceive semantic features.(3)We explore the guiding role of introducing different types of additional information for image inpainting model l2 earning.For the first inpainting model,we use a two-stage inpainting approach and introduce an edge map that represents structural features as input for rough inpainting in the first stage,and introduce a high-frequency residual map that represents texture features as input for fine inpainting in the second stage.For the second inpainting model,we adopt the method of embedding saved high-level semantic features as additional information into the model after feature extraction.This strategy can help us promote the improvement of feature extraction and enhance the performance of the inpainting process.