Adaptive Image Restoration for Video Surveillance: A Real-Time Approach
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912382133993472 |
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| author | Amin, Muhammad Awais Ilboudo, Adama Shahid, Abdul Samad bin Ali, Amjad Bangyal, Waqas Haider Khan |
| author_facet | Amin, Muhammad Awais Ilboudo, Adama Shahid, Abdul Samad bin Ali, Amjad Bangyal, Waqas Haider Khan |
| contents | One of the major challenges in the field of computer vision especially for detection, segmentation, recognition, monitoring, and automated solutions, is the quality of images. Image degradation, often caused by factors such as rain, fog, lighting, etc., has a negative impact on automated decision-making.Furthermore, several image restoration solutions exist, including restoration models for single degradation and restoration models for multiple degradations. However, these solutions are not suitable for real-time processing. In this study, the aim was to develop a real-time image restoration solution for video surveillance. To achieve this, using transfer learning with ResNet_50, we developed a model for automatically identifying the types of degradation present in an image to reference the necessary treatment(s) for image restoration. Our solution has the advantage of being flexible and scalable. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_13130 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive Image Restoration for Video Surveillance: A Real-Time Approach Amin, Muhammad Awais Ilboudo, Adama Shahid, Abdul Samad bin Ali, Amjad Bangyal, Waqas Haider Khan Computer Vision and Pattern Recognition Artificial Intelligence One of the major challenges in the field of computer vision especially for detection, segmentation, recognition, monitoring, and automated solutions, is the quality of images. Image degradation, often caused by factors such as rain, fog, lighting, etc., has a negative impact on automated decision-making.Furthermore, several image restoration solutions exist, including restoration models for single degradation and restoration models for multiple degradations. However, these solutions are not suitable for real-time processing. In this study, the aim was to develop a real-time image restoration solution for video surveillance. To achieve this, using transfer learning with ResNet_50, we developed a model for automatically identifying the types of degradation present in an image to reference the necessary treatment(s) for image restoration. Our solution has the advantage of being flexible and scalable. |
| title | Adaptive Image Restoration for Video Surveillance: A Real-Time Approach |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2505.13130 |