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Gamma spectrum denoising method based on improved wavelet threshold

机译:基于改进小波阈值的伽马频谱去噪方法

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Adverse effects in the measured gamma spectrum caused by radioactive statistical fluctuations, gamma ray scattering, and electronic noise can be reduced by energy spectrum denoising. Wavelet threshold denoising can be used to perform multi-scale and multi-resolution analysis on noisy signals with small root mean square errors and high signal-to-noise ratios. However, in traditional wavelet threshold denoising methods, there are signal oscillations in hard threshold denoising and constant deviations in soft threshold denoising. An improved wavelet threshold calculation method and threshold processing function are proposed in this paper. The improved threshold calculation method takes into account the influence of the number of wavelet decomposition layers and reduces the deviation caused by the inaccuracy of the threshold. The improved threshold processing function can be continuously guided, which solves the discontinuity of the traditional hard threshold function, avoids the constant deviation caused by the traditional soft threshold method. The examples show that the proposed method can accurately denoise and preserves the characteristic signals well in the gamma energy spectrum.
机译:通过节能谱差异,可以减少由放射性统计波动,伽马射线散射和电子噪声引起的测量伽马光谱中的不利影响。小波阈值去噪可用于对具有小根均方误差和高信噪比的噪声信号进行多尺度和多分辨率分析。然而,在传统的小波阈值去噪方法中,在软阈值去噪中存在硬阈值的信号振荡和恒定的偏差。本文提出了一种改进的小波阈值计算方法和阈值处理功能。改进的阈值计算方法考虑了小波分解层数的影响,并降低了由阈值不准确引起的偏差。可以连续地引导改进的阈值处理功能,其解决了传统硬阈值函数的不连续性,避免了由传统的软阈值方法引起的恒定偏差。该示例表明,该方法可以准确地欺骗并保留在伽马能谱中的特征信号。

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