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dc.contributorDepartment of Electronic and Information Engineeringen_US
dc.contributor.advisorChi, Zheru (EIE)en_US
dc.creatorChen, Shengyang-
dc.identifier.urihttps://theses.lib.polyu.edu.hk/handle/200/11180-
dc.languageEnglishen_US
dc.publisherHong Kong Polytechnic Universityen_US
dc.rightsAll rights reserveden_US
dc.titlePhoto inpainting with GAN modelsen_US
dcterms.abstractPhoto inpainting is an important task in photo editing. Its purpose is to recover the corrupted photos. Many methods have been proposed to solve different kinds of photo inpainting problems. The Context Encoder (CE) is the one of the most important ANN based methods. However, its performance is not very good because it may generate contents which are not semantically coherent enough. In this dissertation, I introduce my method of integrating the dilation convolution into the original context encoder to improve the photo inpainting performance. The performance evaluation is done by using not only MSE and PSNR, but also SSIM to measure the similarity between the generated content and the original content.en_US
dcterms.extentiv, 35 pages : color illustrationsen_US
dcterms.isPartOfPolyU Electronic Thesesen_US
dcterms.issued2021en_US
dcterms.educationalLevelM.Sc.en_US
dcterms.educationalLevelAll Masteren_US
dcterms.LCSHPhotography -- Digital techniquesen_US
dcterms.LCSHImage processing -- Digital techniquesen_US
dcterms.LCSHImage reconstructionen_US
dcterms.LCSHHong Kong Polytechnic University -- Dissertationsen_US
dcterms.accessRightsrestricted accessen_US

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Please use this identifier to cite or link to this item: https://theses.lib.polyu.edu.hk/handle/200/11180