降噪
计算机科学
噪音(视频)
人工智能
信号处理
信号(编程语言)
卷积神经网络
视频去噪
模式识别(心理学)
数字信号处理
视频处理
计算机硬件
多视点视频编码
图像(数学)
程序设计语言
视频跟踪
作者
Maoning Wang,Lin Deng,Yuzhong Zhong,Jianwei Zhang,Fei Peng
标识
DOI:10.1109/jlt.2021.3052651
摘要
In most optical fiber distributed acoustic sensing (DAS) systems, to obtain the desired outcome, the sensing signal acquired by DAS systems normally needs to be denoised. In some applications, such as the identifiation of the position of fast-moving targets, we need DAS systems to respond with sufficient speed. However, most classical denoising algorithms do not work if the signals are insufficiently collected within a short period (called a short-time signal). To obtain ideal results within a short time window, we propose an attention-based convolutional neural network (CNN) structure with extremely short signal windows to learn and approximate the results of classical denoising methods. To evaluate the effectiveness of the proposed method, the experiment is conducted under a real field highway scenario where the desired signals are overwhelmed with noise. The results show that by using signals collected within extremely short time windows of 100 ms, an insufficient time for the processing of existing denoising algorithms, our structure yields a satisfactory denoising performance.
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