| Popis: |
According to the "White Paper on China's Intelligent Internet of Things (AIoT) 2020",with the prompt development of China's 5G network,the rapid popularization of large-capacity with low-price IoT sensor devices and the explosive growth of data,image processing is widely used in various fields of Internet of Things,such as smart city,smart transportation,smart healthcare,and other industry,etc.In these research areas,researchers usually ignore the actual problems in the data collection process,for instance,data degradation caused by time changes:seasonal shifting,diurnal variation,weather changes,and noise problems caused by spatial changes:object superposition,blur,and partial occlusion.Among those problems,the weather pro-blems represented by rainy days are the most challenging and common.Therefore,this paper systematically investigates the actual problems in the data collection process above,classifies and summarizes the image rain-removal algorithms under complex weather conditions.At the same time,regarding the compute-intensive execution of such algorithms,we utilize the Amazon EC2 cloud instance G4 and P3 series to quantitatively evaluate the processing time and effect of various reviewed rain removal algorithms.Finally,we illustrate the characteristics of various rain removal algorithms and the latest trends in Cloud-IoT applications. |
