Wearable Optical Imaging Devices Based on Wireless Sensor Networks and Fuzzy Image Restoration Algorithms for Sports Image Analysis

Linyan Li
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Abstract

With the rapid development of Internet of Things technology, wearable optical imaging devices can monitor the status and performance of athletes in real time, but the image quality is affected by environmental factors, often resulting in information loss. This study aims to improve the effectiveness of wearable optical imaging devices in sports image analysis by using wireless sensor networks and fuzzy image recovery algorithms, so as to achieve more accurate motion state monitoring. Wireless sensor network architecture combined with mobile network technology is used to realize data acquisition and transmission in motion scenes. In this paper, a fuzzy image recovery algorithm is designed and implemented to process fuzzy image data collected by equipment. In the experiment, the algorithm is trained and verified by using the image data in different motion scenes, and its recovery effect is analyzed. Experiments show that the proposed fuzzy image recovery algorithm can effectively improve the clarity and detail capture of images, and make the status monitoring of athletes more timely and reliable combined with the real-time data transmission of wireless sensor networks. Therefore, wearable optical imaging equipment based on wireless sensor network and fuzzy image recovery algorithm shows a good application prospect in sports image analysis, which can provide important support for athletes’ training and performance evaluation, and promote the intelligent process in the field of sports.

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基于无线传感器网络的可穿戴光学成像设备和用于运动图像分析的模糊图像复原算法
随着物联网技术的快速发展,可穿戴光学成像设备可以实时监测运动员的状态和表现,但图像质量受环境因素影响较大,往往会造成信息丢失。本研究旨在利用无线传感器网络和模糊图像复原算法提高可穿戴光学成像设备在运动图像分析中的有效性,从而实现更精确的运动状态监测。本文采用无线传感器网络架构,结合移动网络技术,实现运动场景中的数据采集和传输。本文设计并实现了一种模糊图像复原算法,用于处理设备采集到的模糊图像数据。实验中,利用不同运动场景的图像数据对算法进行了训练和验证,并分析了其恢复效果。实验表明,所提出的模糊图像复原算法能有效提高图像的清晰度和细节捕捉能力,结合无线传感器网络的实时数据传输,使运动员的状态监测更加及时可靠。因此,基于无线传感器网络和模糊图像复原算法的可穿戴光学成像设备在体育图像分析中具有良好的应用前景,可为运动员的训练和成绩评估提供重要支持,推动体育领域的智能化进程。
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