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在分析双模噪声模型统计特性的基础上提出自适应小波阈值算法。新算法中设计改进的阈值函数和控制函数,克服了传统硬、软阈值法的不足,并且自适应得到最佳控制因子。该算法对加入双模噪声的信号进行闭环反馈处理:小波分解、阈值量化处理、小波逆变换重构信号、控制函数寻优。Matlab 2012a仿真结果表明,该算法相对于传统硬、软阈值法,去噪图形曲线清晰、光滑、连续性好,信噪比分别提高9 dB和4 dB。在双模噪声背景下,自适应小波阈值去噪有效、可行,拓展了小波阈值算法的应用。
Based on the analysis of the statistical properties of the dual-mode noise model, an adaptive wavelet threshold algorithm is proposed. The new algorithm is designed to improve the threshold function and control function to overcome the shortcomings of the traditional hard and soft threshold method, and get the best adaptive control factor. The algorithm performs closed-loop feedback processing on the signal with dual-mode noise added: wavelet decomposition, threshold quantization, inverse wavelet transform signal reconstruction, control function optimization. Matlab 2012a simulation results show that compared with the traditional hard and soft thresholding method, the proposed algorithm has a clear, smooth, continuous and good signal-to-noise ratio of 9 dB and 4 dB respectively. In the background of dual-mode noise, adaptive wavelet threshold denoising is effective and feasible, and extends the application of wavelet threshold algorithm.