Adaptive Image Restoration for Video Surveillance: A Real-Time Approach

TL;DR

This paper presents a real-time, scalable image restoration method for video surveillance that automatically identifies degradation types using transfer learning with ResNet-50, improving automated decision-making in challenging conditions.

Contribution

Developed a novel real-time image restoration approach that automatically detects degradation types and applies appropriate restoration, suitable for video surveillance applications.

Findings

Effective degradation identification using transfer learning.

Real-time processing capability demonstrated.

Flexible and scalable restoration framework.

Abstract

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.