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...

Full description

Saved in:
Bibliographic Details
Main Author: Huang, QiYuan
Other Authors: He Ying
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/175204
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-175204
record_format dspace
spelling sg-ntu-dr.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
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Computer and Information Science
Engineering
Computer science and engineering
spellingShingle Computer and Information Science
Engineering
Computer science and engineering
Huang, QiYuan
Colour transfer between images
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.
author2 He Ying
author_facet He Ying
Huang, QiYuan
format Final Year Project
author Huang, QiYuan
author_sort Huang, QiYuan
title Colour transfer between images
title_short Colour transfer between images
title_full Colour transfer between images
title_fullStr Colour transfer between images
title_full_unstemmed Colour transfer between images
title_sort colour transfer between images
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/175204
_version_ 1806059838977867776