{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T05:31:05Z","timestamp":1771047065956,"version":"3.50.1"},"reference-count":49,"publisher":"World Scientific Pub Co Pte Ltd","issue":"12","funder":[{"name":"Natural Science Foundation of Shaanxi","award":["2022JM-317"],"award-info":[{"award-number":["2022JM-317"]}]},{"name":"Natural Science Foundation of Shaanxi","award":["2019JM-162"],"award-info":[{"award-number":["2019JM-162"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2022,8]]},"abstract":"<jats:p> In order to address the problems of traditional inpainting algorithm models, such as the inability to automatically identify the specific location of the area to be restored, the cost of inpainting and the difficulty of inpainting, and the problems of structural and texture discontinuity and poor model stability in deep learning-based image inpainting, this paper proposes an image inpainting based on a contextual coherent attention. This paper designs a network model based on generative adversarial networks. First, to improve the global semantic continuity and local semantic continuity of images in image inpainting, a contextual coherent attention layer is added to the network; second, to solve the problems of slow convergence and insufficient training stability of the model, a cross-entropy loss function is used; finally, the trained generator is used to repair images. The experimental results are compared using PSNR and SSIM metrics, compared with the traditional GAN model, our model has a 3.782dB improvement in peak signal-to-noise ratio and a 0.025% improvement in structural similarity. The experimental results show that the image inpainting method in this paper has better performance in terms of image edge processing, pixel continuity and overall image structure. <\/jats:p>","DOI":"10.1142\/s0218126622502097","type":"journal-article","created":{"date-parts":[[2022,4,29]],"date-time":"2022-04-29T14:50:28Z","timestamp":1651243828000},"source":"Crossref","is-referenced-by-count":8,"title":["Image Inpainting Based on Contextual Coherent Attention GAN"],"prefix":"10.1142","volume":"31","author":[{"given":"Hong-an","family":"Li","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, P.\u00a0R.\u00a0China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2058-482X","authenticated-orcid":false,"given":"Liuqing","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, P.\u00a0R.\u00a0China"}]},{"given":"Qiaozhi","family":"Hua","sequence":"additional","affiliation":[{"name":"Computer School, Hubei University of Arts and Science, Xiangyang 441000, P.\u00a0R.\u00a0China"}]},{"given":"Meng","family":"Yang","sequence":"additional","affiliation":[{"name":"Xi\u2019an Institute of Applied Optics, Xi\u2019an 710065, P.\u00a0R.\u00a0China"}]},{"given":"Xinpeng","family":"Li","sequence":"additional","affiliation":[{"name":"Beijing Municipal Administration of Government Logistics, Beijing 100010, P.\u00a0R.\u00a0China"}]}],"member":"219","published-online":{"date-parts":[[2022,4,27]]},"reference":[{"key":"S0218126622502097BIB001","doi-asserted-by":"publisher","DOI":"10.1016\/j.displa.2021.102028"},{"key":"S0218126622502097BIB002","doi-asserted-by":"publisher","DOI":"10.1109\/TNSE.2021.3101842"},{"key":"S0218126622502097BIB003","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3070544"},{"key":"S0218126622502097BIB004","author":"Peng Y.","year":"2021","journal-title":"IEEE Trans. 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