Adaptive Image Restoration for Video Surveillance: A Real-Time Approach
This addresses image quality issues for automated decision-making in video surveillance, but it appears incremental as it builds on existing restoration models with a focus on real-time processing.
The study tackled the problem of image degradation in video surveillance by developing a real-time image restoration solution using transfer learning with ResNet_50 to automatically identify degradation types, achieving unspecified performance metrics.
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.