Deep image inpainting
Over the years, many techniques have emerged to reconstruct and modify images for a myriad of applications. One ingenious application is image inpainting, which is to restore the missing parts of an image. The latest approach employs deep learning technique to solve the problem. Deep convolutional n...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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2019
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| Online Access: | http://hdl.handle.net/10356/79001 |
| _version_ | 1826114377678323712 |
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| author | Chua, Hao Yang |
| author2 | Chen Change Loy |
| author_facet | Chen Change Loy Chua, Hao Yang |
| author_sort | Chua, Hao Yang |
| collection | NTU |
| description | Over the years, many techniques have emerged to reconstruct and modify images for a myriad of applications. One ingenious application is image inpainting, which is to restore the missing parts of an image. The latest approach employs deep learning technique to solve the problem. Deep convolutional neural networks are used to capture the abstract details of many training images, so that it can guess the context of a missing region. The performance of the network heavily relies on the information provided upon training. Most work failed to utilize or realize the importance of prior information that may boost the proficiency of neural networks. This project attempts to use segmentation maps as a feature engineering to create supplementary information to aid the image inpainting process. The method of inpainting process proposed will consist of two stages. First is to generate the segmentation maps of the missing region. Second is to take the prior segmentation maps generated for fusion into the inpainting process. Training and evaluation are done on ADE20K dataset with eight categories of segmentation defined and all other bodies as the background category. |
| first_indexed | 2024-10-01T03:38:31Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/79001 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:38:31Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/790012023-03-03T20:48:34Z Deep image inpainting Chua, Hao Yang Chen Change Loy School of Computer Science and Engineering Engineering::Computer science and engineering Over the years, many techniques have emerged to reconstruct and modify images for a myriad of applications. One ingenious application is image inpainting, which is to restore the missing parts of an image. The latest approach employs deep learning technique to solve the problem. Deep convolutional neural networks are used to capture the abstract details of many training images, so that it can guess the context of a missing region. The performance of the network heavily relies on the information provided upon training. Most work failed to utilize or realize the importance of prior information that may boost the proficiency of neural networks. This project attempts to use segmentation maps as a feature engineering to create supplementary information to aid the image inpainting process. The method of inpainting process proposed will consist of two stages. First is to generate the segmentation maps of the missing region. Second is to take the prior segmentation maps generated for fusion into the inpainting process. Training and evaluation are done on ADE20K dataset with eight categories of segmentation defined and all other bodies as the background category. Bachelor of Engineering (Computer Science) 2019-11-22T12:23:10Z 2019-11-22T12:23:10Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/79001 en Nanyang Technological University 43 p. application/pdf |
| spellingShingle | Engineering::Computer science and engineering Chua, Hao Yang Deep image inpainting |
| title | Deep image inpainting |
| title_full | Deep image inpainting |
| title_fullStr | Deep image inpainting |
| title_full_unstemmed | Deep image inpainting |
| title_short | Deep image inpainting |
| title_sort | deep image inpainting |
| topic | Engineering::Computer science and engineering |
| url | http://hdl.handle.net/10356/79001 |
| work_keys_str_mv | AT chuahaoyang deepimageinpainting |