基于深度学习的图像降噪技术在智慧水利中的应用

Deep Learning-based Image Noise Reduction Technology in Smart Water Conservancy Application

随着物联网技术的发展,智慧水利系统依赖于从各类监控设备、无人机、摄像头和卫星采集的大量图像数据,这些数据的质量直接影响数字孪生平台中模型的鲁棒性和识别率。文章探讨了图像降噪技术在智慧水利系统中的应用,特别是在数字孪生平台中的重要性,详细分析了图像降噪技术的发展历程,从传统方法到基于深度学习的现代方法,并着重讨论了单帧和多帧降噪技术在处理低光照和复杂天气条件下江河湖海图像的效果。通过实验研究,展示了这些技术在提高图像质量、增强模型性能方面的潜力,强调了在智慧水利系统中综合应用这些技术的重要性。

With the development of Internet of Things(IoT)technology,the intelligent water system relies on a large amount of image data collected from various types of surveillance devices,drones,cameras,and satellites,the quality of which directly affects the robustness and recognition rate of the models in the digital twin platform.The article discusses the application of image denoising in smart water conservancy systems,especially the importance of digital twin platforms,analyzes in detail the development of image denoising from traditional methods to modern methods based on deep learning,and focuses on the effectiveness of single-frame and multi-frame denoising for processing images of river,lake,and sea under low-light and complex weather conditions.The experimental study demonstrates the potential of these techniques to improve image quality and enhance model performance,and emphasizes the importance of integrating these techniques in intelligent water systems.