重庆工商大学学报（自然科学版）2025，Vol.42Issue(4)：9-16,8.DOI:10.16055/j.issn.1672-058X.2025.0004.002
基于双判别器的Wasserstein生成对抗网络图像修复
Image Inpainting Using Wasserstein Generative Adversarial Networks with Dual Discriminators
摘要
Abstract
Objective Image inpainting models based on generative adversarial networks(GANs)suffer from deficiencies when repairing irregular and extensively damaged images,including inaccurate restoration of fine image details,unsatisfactory visual effects,and training instability.In this regard,a Wasserstein GAN-based image inpainting model with dual discriminators was proposed.Methods This method employed a convolutional neural network(CNN)with an encoder-decoder architecture as the generator,with skip connections added between the encoder and decoder of the generator to better learn subtle image features and enhance the final inpainting results.Additionally,a local discriminator network was introduced on the basis of the global discriminator to ensure consistency between locally restored areas and their surroundings.Wasserstein distance was incorporated into the discriminators to stabilize the training process and achieve more natural image inpainting.Furthermore,a VGG16 feature extractor was designed to introduce perceptual and style losses to improve image inpainting by incorporating multiple loss functions.Results Comparative analysis on public datasets of faces,scenes,and streets showed qualitative and quantitative superiority of the proposed method over baseline models,with higher performance evaluation metrics.Conclusion Experimental results demonstrate that the proposed method presents better visual effects and clearer restoration when repairing images with irregular and extensive damaged areas.关键词
图像修复/生成对抗网络/跳跃连接/VGG-16特征提取模型/Wasserstein距离Key words
image inpainting/generative adversarial networks/skip connections/VGG-16 feature extractor/Wasserstein distance分类
信息技术与安全科学引用本文复制引用
李筱玉,张乾,徐开丽,骆迪,冉娅琴..基于双判别器的Wasserstein生成对抗网络图像修复[J].重庆工商大学学报（自然科学版）,2025,42(4):9-16,8.基金项目
贵州民族大学校级科研项目(GZMUZK[2021]YB23) （GZMUZK[2021]YB23）
黔教合(YJSKYJJ[2021]121). （YJSKYJJ[2021]121）
