期刊论文

Chen, Yuantao

英文

VISUAL COMPUTER

0178-2789

2021

37

7

1691-1705

10.1007/s00371-020-01932-3

National Natural Science Foundation of ChinaNational Natural Science Foundation of China [61972056, 61772454, 61402053, 61981340416]; Natural Science Foundation of Hunan Province of ChinaNatural Science Foundation of Hunan Province [2020JJ4623]; Scientific Research Fund of Hunan Provincial Education DepartmentHunan Provincial Education Department [17A007, 19C0028, 19B005]; Changsha Science and Technology Planning [KQ1703018, KQ1706064, KQ1703018-01, KQ1703018-04]; Junior Faculty Development Program Project of Changsha University of Science and Technology [2019QJCZ011]; "Double First-class" International Cooperation and Development Scientific Research Project of Changsha University of Science and Technology [2019IC34]; Practical Innovation and Entrepreneurship Ability Improvement Plan for Professional Degree Postgraduate of Changsha University of Science and Technology [SJCX202072]; Postgraduate Training Innovation Base Construction Project of Hunan Province [2019-248-51]

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Existing image inpainting algorithms based on neural network models are affected by structural distortions and blurred textures on visible connectivity. As a result, overfitting and overlearning phenomena can easily emerge during the image inpainting procedure. Image inpainting refers to the repairing of missing parts of an image, given an image that is broken or incomplete. After the repairing operation is complete, there are obvious signs of repair in damaged areas, semantic discontinuities, and unclearness. This paper proposes an improved image inpainting method based on a new encoder combi...