噪音(视频)
随机共振
时频分析
信噪比(成像)
降噪
短时傅里叶变换
卷积神经网络
傅里叶变换
谐波
计算机科学
噪声测量
声学
自由感应衰变
核磁共振
物理
人工智能
语音识别
傅里叶分析
磁共振成像
电信
雷达
自旋回波
医学
量子力学
图像(数学)
放射科
作者
Chuandong Jiang,Ruixin Miao,Bang Li,Baofeng Tian,Xinlei Shang,Qingming Duan,Tingting Lin
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:72: 1-11
标识
DOI:10.1109/tim.2023.3250299
摘要
Magnetic resonance sounding (MRS) is one of the technical applications of nuclear magnetic resonance (NMR) used to directly detect and quantify groundwater content. MRS suffers from a low signal-to-noise ratio (SNR) due to the low amplitude of free induction decay (FID) signals and an inability to shield environmental noise. In this article, a time–frequency fully convolutional neural network (TFCN) was proposed to suppress random, harmonic, and spike noise from MRS data. The TFCN parameters were trained with the time–frequency spectrum obtained by the short-time Fourier transform (STFT) of the MRS datasets as the input and the noise-free FID signals as the output. Based on the results of synthetic and field data experiments, the TFCN was compared with existing denoising methods. The results showed that the TFCN extracted the envelope of the FID signals from low-SNR random noise with higher accuracy than other methods. Moreover, the TFCN simultaneously suppressed multiple types of noise and exhibited high computational efficiency.
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