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The difficulty of image generation and completion lies in the reasonableness of image semantics and the clear and true texture of the generated image. In this paper, a Wasserstein generative adversarial network with dilated convolution and deformable convolution (DDC-WGAN) is proposed for image completion. A deformable offset is added based on dilated convolution, which enlarges the receptive field and provides a more stable representation of geometric deformation. Experiments show that the DDC-WGAN method proposed in this paper has better performance in image generation and complementation than the traditional generative adversarial complementation network. <\/jats:p>","DOI":"10.1142\/s0218126622501146","type":"journal-article","created":{"date-parts":[[2021,12,12]],"date-time":"2021-12-12T05:52:25Z","timestamp":1639288345000},"source":"Crossref","is-referenced-by-count":4,"title":["Generative Image Inpainting with Dilated Deformable Convolution"],"prefix":"10.1142","volume":"31","author":[{"given":"Zhao","family":"Qiu","sequence":"first","affiliation":[{"name":"Computer Science and Technology, Hainan University, Haikou 570228, P. R. China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1505-8611","authenticated-orcid":false,"given":"Lin","family":"Yuan","sequence":"additional","affiliation":[{"name":"Computer Science and Technology, Hainan University, Haikou 570228, P. R. 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