Image Inpainting Based on Improved Deep Convolutional Auto‐encoder Network

Chinese Journal of ElectronicsVolume 29, Issue 6 p. 1074-1084 ArticleFree Access Image Inpainting Based on Improved Deep Convolutional Auto-encoder Network Zhenping Qiang, Corresponding Author qzp@swfu.edu.cn College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, 650224 ChinaSearch for more papers by this authorLibo He, Information Security College, Yunnan Police College, Kunming, 650223 ChinaSearch for more papers by this authorFei Dai, College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, 650224 ChinaSearch for more papers by this authorQinghui Zhang, College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, 650224 ChinaSearch for more papers by this authorJunqiu Li, College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, 650224 ChinaSearch for more papers by this author Zhenping Qiang, Corresponding Author qzp@swfu.edu.cn College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, 650224 ChinaSearch for more papers by this authorLibo He, Information Security College, Yunnan Police College, Kunming, 650223 ChinaSearch for more papers by this authorFei Dai, College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, 650224 ChinaSearch for more papers by this authorQinghui Zhang, College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, 650224 ChinaSearch for more papers by this authorJunqiu Li, College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, 650224 ChinaSearch for more papers by this author First published: 01 November 2020 https://doi.org/10.1049/cje.2020.09.008AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Abstract This paper proposes an effective image inpainting method using an improved deep convolutional auto-encoder network. By analogy with exiting methods of image inpainting based on auto-decoders, inpainting methods using the deep convolutional auto-encoder networks are significantly more effective in capturing high-level features than classical methods based on exemplar. However, the inpainted regions would appear blurry and global inconsistency. To alleviate the fuzzy problem, we improved the network model by adding skip connections between mirrored layers in encoder and decoder stacks, so that the generative process of the inpainting area can directly use the low-level features information of the processing image. For making the inpainted result look both more plausible and consistent with its surrounding contexts, the model is trained with a combination of standard pixel-wise reconstruction loss and two adversarial losses which ensures pixel-accurate and local-global contents consistency. With extensive experimental on the ImageNet and Paris Streetview datasets, we demonstrate qualitatively and quantitatively that our approach performs better than state of the art. Volume29, Issue6November 2020Pages 1074-1084 RelatedInformation