Author: Chen, Shengyang
Title: Photo inpainting with GAN models
Advisors: Chi, Zheru (EIE)
Degree: M.Sc.
Year: 2021
Subject: Photography -- Digital techniques
Image processing -- Digital techniques
Image reconstruction
Hong Kong Polytechnic University -- Dissertations
Department: Department of Electronic and Information Engineering
Pages: iv, 35 pages : color illustrations
Language: English
Abstract: Photo 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.
Rights: All rights reserved
Access: restricted access

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