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Image Completion with Large or Edge-Missing Areas

School of Information Science & Technology, Beijing Forestry University, Beijing 100083, China
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Algorithms 2020, 13(1), 14; https://doi.org/10.3390/a13010014
Received: 3 December 2019 / Revised: 22 December 2019 / Accepted: 26 December 2019 / Published: 31 December 2019
Existing image completion methods are mostly based on missing regions that are small or located in the middle of the images. When regions to be completed are large or near the edge of the images, due to the lack of context information, the completion results tend to be blurred or distorted, and there will be a large blank area in the final results. In addition, the unstable training of the generative adversarial network is also prone to cause pseudo-color in the completion results. Aiming at the two above-mentioned problems, a method of image completion with large or edge-missing areas is proposed; also, the network structures have been improved. On the one hand, it overcomes the problem of lacking context information, which thereby ensures the reality of generated texture details; on the other hand, it suppresses the generation of pseudo-color, which guarantees the consistency of the whole image both in vision and content. The experimental results show that the proposed method achieves better completion results in completing large or edge-missing areas. View Full-Text
Keywords: image completion; generative adversarial networks; large missing area; edge-missing area image completion; generative adversarial networks; large missing area; edge-missing area
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Ji, J.; Yang, G. Image Completion with Large or Edge-Missing Areas. Algorithms 2020, 13, 14.

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