实时视频监控中自动识别并修复图像退化，提升智能分析准确性。
Adaptive Image Restoration for Video Surveillance: A Real-Time Approach
- 基于ResNet_50的迁移学习模型，自动识别图像退化类型。
- 支持多类退化处理，满足实时视频流处理需求。
- 灵活可扩展，适合部署在实际安防系统中。
计算机视觉在检测、分割、识别、监控及自动化解决方案中面临的主要挑战之一是图像质量。图像退化（如雨、雾、光照不良等）对自动化决策产生负面影响。现有修复方法包括针对单一退化和多种退化的模型，但均不适用于实时处理。本研究旨在开发一种适用于视频监控的实时图像修复方案。通过使用基于ResNet_50的迁移学习，构建了可自动识别图像退化类型的模型，以确定相应的修复策略。该方法具有灵活性与可扩展性，适用于实际场景中的实时视频处理。
原文摘要 · Abstract (English)
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.
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