小波分析在信号处理中的应用设计说明

小 波 分 析 在 信 号 处 理 中 的 应 用摘 要小波分析是纯数学、应用数学和工程技术的完美结合。小波变换在于音频信号图像信号的处理中具有重要的意义。在传统的傅立叶分析中，信号完全是在频域展开的，不包含任何时频的信息，这对于某些应用来说是很恰当的，因为信号的频率的信息对其是非常重要的。但其丢弃的时域信息可能对某些应用同样非常重要。而小波分析则克服了短时傅立叶变换在单分辨率上的缺陷，具有多分辨率分析的特点，在时域和频域都有表征信号局部信息的能力。而在于信号之中图像是一种重要的信息源，通过图像处理可以帮助人们了解信息的涵。本文简述了小波包分析的原理，并基于 MATLAB 实现了对二维图像信号进行消噪。对常用的几种阈值去噪方法进行了分析比较和仿真实现。最后结合理论分析和实验结果，讨论了去噪过程中影响去噪性能的各种因素。为在实际的图像处理中，小波包阈值去噪法的选择和改进提供了数据参考和依据关键词：信号；图像锐化；图像去噪；小波分析CC 所 有 仅 供 参 考 ！ ！ ！The application of wavelet analysis in signal processingABSTRACTWavelet analysis is pure mathematics, applied mathematics and engineering the perfect combination. Wavelet transform is the audio signal processing of the image signal has an important significance. In conventional Fourier analysis, the signal is completely expanded in the frequency domain, the frequency does not contain any information, which for some applications is very appropriate because of its frequency of the signal information is very important. But its time-domain information may be discarded for certain applications is also very important.The wavelet analysis is to overcome the short-time Fourier transform in a single resolution of defects, with the multi-resolution analysis of the characteristics of the time domain and frequency domain signals are characterized by the ability of local information. But rather among the image signal is an important source of information, through image processing can...