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 |
| Language: | English |
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Nanyang Technological University
2024
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| Online Access: | https://hdl.handle.net/10356/176732 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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. |
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