Application of Generative Adversarial Networks to Image Inpainting
| Autor: | Jiang Jian Wei, 江建衛 |
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| Rok vydání: | 2019 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 107 Based on the works of Pathak et al. [13] and Iizuka et al. [6], in this thesis, we introduce a simple generative adversarial network approach for image inpainting. Considering the limitation of computational capacity, we build a simplified model which is able to reconstruct lost or deteriorated parts of images with single context and small missing region. In order to generate the image content of missing region, we mainly employ the generative adversarial network approach proposed by Goodfellow et al. [4]. More specifically, the proposed neural network consists of convolutional layers, where the dilated convolution is used in the generative network. In addition, except the output layer, each layer is equipped with a normalization layer [7] to enhance the overall efficiency of the network. Numerical experiments are performed to demonstrate the good performance of the simplified generative adversarial network for image inpainting. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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