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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| 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/10356/176732 |
| _version_ | 1826119826567856128 |
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| author | Li, Siyang |
| author2 | Jiang Xudong |
| author_facet | Jiang Xudong Li, Siyang |
| author_sort | Li, Siyang |
| collection | NTU |
| 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. |
| first_indexed | 2024-10-01T05:06:28Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/176732 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:06:28Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-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 |
| spellingShingle | Engineering Li, Siyang Low-light image enhancement based on machine learning |
| title | 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_short | Low-light image enhancement based on machine learning |
| title_sort | low light image enhancement based on machine learning |
| topic | Engineering |
| url | https://hdl.handle.net/10356/176732 |
| work_keys_str_mv | AT lisiyang lowlightimageenhancementbasedonmachinelearning |