Low-light image enhancement based on machine learning

This project focuses on low-light image enhancement using machine learning techniques. Images captured in low-light conditions often suffer from low contrast, poor visibility, and unexpected noise, leading to unpleasant subjective feelings and hindering the performance of computer vision tasks like...

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Bibliographic Details
Main Author: Li, Siyang
Other Authors: Jiang Xudong
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/176732
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Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-176732
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spelling sg-ntu-dr.10356-1767322024-05-24T15:50:52Z Low-light image enhancement based on machine learning Li, Siyang Jiang Xudong School of Electrical and Electronic Engineering EXDJiang@ntu.edu.sg Engineering This project focuses on low-light image enhancement using machine learning techniques. Images captured in low-light conditions often suffer from low contrast, poor visibility, and unexpected noise, leading to unpleasant subjective feelings and hindering the performance of computer vision tasks like object detection and scene understanding. To address these issues, this project explores methods that utilize deep neural networks and classical nonlinear point processing techniques to enhance low-light images. The Zero DCE-Net model is used for training and evaluation. The findings demonstrate the model's efficiency in addressing the challenges of traditional techniques and its potential to improve various computer vision applications. The successful development and evaluation of Zero DCE-Net open avenues for future research and applications in real-world scenarios. Bachelor's degree 2024-05-20T02:25:58Z 2024-05-20T02:25:58Z 2024 Final Year Project (FYP) Li, S. (2024). Low-light image enhancement based on machine learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/176732 https://hdl.handle.net/10356/176732 en P3032-222 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 Engineering
spellingShingle Engineering
Li, Siyang
Low-light image enhancement based on machine learning
description This project focuses on low-light image enhancement using machine learning techniques. Images captured in low-light conditions often suffer from low contrast, poor visibility, and unexpected noise, leading to unpleasant subjective feelings and hindering the performance of computer vision tasks like object detection and scene understanding. To address these issues, this project explores methods that utilize deep neural networks and classical nonlinear point processing techniques to enhance low-light images. The Zero DCE-Net model is used for training and evaluation. The findings demonstrate the model's efficiency in addressing the challenges of traditional techniques and its potential to improve various computer vision applications. The successful development and evaluation of Zero DCE-Net open avenues for future research and applications in real-world scenarios.
author2 Jiang Xudong
author_facet Jiang Xudong
Li, Siyang
format Final Year Project
author Li, Siyang
author_sort Li, Siyang
title Low-light image enhancement based on machine learning
title_short Low-light image enhancement based on machine learning
title_full Low-light image enhancement based on machine learning
title_fullStr Low-light image enhancement based on machine learning
title_full_unstemmed Low-light image enhancement based on machine learning
title_sort low-light image enhancement based on machine learning
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/176732
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