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  • 标题:Improved image inpainting exemplar-based algorithms by boundary priori-knowledge
  • 本地全文:下载
  • 作者:Junhong Zhao ; Jintao Tan ; Yaobin Huang
  • 期刊名称:MATEC Web of Conferences
  • 电子版ISSN:2261-236X
  • 出版年度:2022
  • 卷号:355
  • 页码:1-7
  • DOI:10.1051/matecconf/202235503004
  • 语种:English
  • 出版社:EDP Sciences
  • 摘要:Image inpainting plays an important role in restoration of cultural relics, pictures beautification. Criminisi algorithm creates good results in large-area inpainting. However, it does still have some deficiencies such as over-extending. In this paper, two improved algorithms based on prior knowledge of the boundary had been proposed by simulating the idea of manual repairing. An algorithm, by simulating the strategy that the next inpainted pixel will be near to the prior one, named nearer neighbor first algorithm, can void the random bounding of the to-be-inpainted pixle. Another algorithm, by simulating the strategy that the inpainting process, named no-inpainted first algorithm, will be in multiple directions, can void the inpainting process in a single direction. The results reveal that the neighborhood-first algorithm performs better than Criminsi algorithm in repairing the missing structure while the unrepaired-first algorithm performs better than Criminsi algorithm in repairing the missing texture.
  • 关键词:Image inpainting;Exemplar-Based;Fill-front;PrioriKnowledge
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