Colour transfer between images
Colour image processing is one the most crucial aspects of image processing, it serves as a method to enhance the visual quality of images. Traditional methods usually focus on altering an image's colour using predefined rules or statistical models. Additionally, it acts as a foundation for...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/175204 |
| _version_ | 1811693278154719232 |
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| author | Huang, QiYuan |
| author2 | He Ying |
| author_facet | He Ying Huang, QiYuan |
| author_sort | Huang, QiYuan |
| collection | NTU |
| description | Colour image processing is one the most crucial aspects of image processing, it serves as a
method to enhance the visual quality of images. Traditional methods usually focus on altering
an image's colour using predefined rules or statistical models. Additionally, it acts as a
foundation for other applications such as medical imaging and object recognition.
In this project, we delve into the concept of colour transfer which involves borrowing colour
characteristics from reference images. Specifically, we study two well-known algorithms for
colour transfer tasks, one by Reinhard et al. and the other by Pitie et al. Next, integrating
advanced semantic segmentation techniques by leveraging state-of-the-art models such as
DeepLab V3 developed by the Google Research team, along with LangSAM, originally
developed by Meta AI as the Segment-Anything Model (SAM) and later adapted into
LangSAM by Luca Medeiros and others, we propose a framework for applying colour
transfer based on these models.
By conducting a comprehensive comparative analysis, we assess the performance and
efficacy of each approach, shedding light on their strengths and limitations in real-world
applications. Our findings aim to provide valuable insights for future research in image
processing. |
| first_indexed | 2024-10-01T06:49:08Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/175204 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:49:08Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1752042024-04-19T15:42:59Z Colour transfer between images Huang, QiYuan He Ying School of Computer Science and Engineering YHe@ntu.edu.sg Computer and Information Science Engineering Computer science and engineering Colour image processing is one the most crucial aspects of image processing, it serves as a method to enhance the visual quality of images. Traditional methods usually focus on altering an image's colour using predefined rules or statistical models. Additionally, it acts as a foundation for other applications such as medical imaging and object recognition. In this project, we delve into the concept of colour transfer which involves borrowing colour characteristics from reference images. Specifically, we study two well-known algorithms for colour transfer tasks, one by Reinhard et al. and the other by Pitie et al. Next, integrating advanced semantic segmentation techniques by leveraging state-of-the-art models such as DeepLab V3 developed by the Google Research team, along with LangSAM, originally developed by Meta AI as the Segment-Anything Model (SAM) and later adapted into LangSAM by Luca Medeiros and others, we propose a framework for applying colour transfer based on these models. By conducting a comprehensive comparative analysis, we assess the performance and efficacy of each approach, shedding light on their strengths and limitations in real-world applications. Our findings aim to provide valuable insights for future research in image processing. Bachelor's degree 2024-04-19T13:37:15Z 2024-04-19T13:37:15Z 2024 Final Year Project (FYP) Huang, Q. (2024). Colour transfer between images. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/175204 https://hdl.handle.net/10356/175204 en SCSE23-0346 application/pdf Nanyang Technological University |
| spellingShingle | Computer and Information Science Engineering Computer science and engineering Huang, QiYuan Colour transfer between images |
| title | Colour transfer between images |
| title_full | Colour transfer between images |
| title_fullStr | Colour transfer between images |
| title_full_unstemmed | Colour transfer between images |
| title_short | Colour transfer between images |
| title_sort | colour transfer between images |
| topic | Computer and Information Science Engineering Computer science and engineering |
| url | https://hdl.handle.net/10356/175204 |
| work_keys_str_mv | AT huangqiyuan colourtransferbetweenimages |