CAJ | 학술논문

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 2012 a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。
재분석쌍모조성모형통계특성적기출상제출자괄응소파역치산법。신산법중설계개진적역치함수화공제함수，극복료전통경、연역치법적불족，병차자괄응득도최가공제인자。해산법대가입쌍모조성적신호진행폐배반궤처리：소파분해、역치양화처리、소파역변환중구신호、공제함수심우。Matlab 2012 a방진결과표명，해산법상대우전통경、연역치법，거조도형곡선청석、광활、련속성호，신조비분별제고9 dB화4 dB。재쌍모조성배경하，자괄응소파역치거조유효、가행，탁전료소파역치산법적응용。
An adaptive wavelet threshold algorithm is put forward through analyzing the statistical character-istics of dual-mode noise model. In the new algorithm,improved threshold function and control function are proposed to overcome the deficiency of the traditional hard and soft threshold method,and get the optimal control factor adaptively. The algorithm performs following closed-loop feedback processing of signal in which dual-mode noise is added :wavelet decomposition and threshold quantization processing,wavelet in-verse transformation to reconstruct signal,and control function optimization. Matlab 2012a simulation results show that the denoising graph curve is clear,smooth and in good continuity,and the signal-to-noise ratio ( SNR) is improved 9 dB and 4 dB,respectively. Under the background of dual-mode noise,adaptive wavelet threshold denoising is effective and feasible,and it extends the application of the wavelet threshold algorithm.