| 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 |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 5655.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 981.39 kB | Adobe PDF | View/Open |
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