An adaptive noise removal tool for IoT image processing under influence of weather conditions
Mingkang Chen, Jingtao Sun, Kazushige Saga, Tomota Tanjo, Kento Aida · 2020
As the foundation of intelligent algorithms and applications, data collection from the real world faces the problem that there is serious data degradation under various complex environments. As a typical situation, the visual degradation of images under different weather conditions only can be utilized after arduous image noise removal by application developers previously. To overcome the challenges, previous approaches cannot handle with comprehensive situations. In this paper, we will briefly describe an adaptive image noise removal tool, which can classify multiple weather conditions and enhance image quality with optimized algorithms. Further, we constructed a recognition application using YOLO-3, and validated the effect of our tool through recognition results of real-world images.