MATLAB Implementation of Wavelet Threshold Denoising with Code Examples

Wavelet threshold denoising is a widely-used signal processing technique that effectively removes noise from signals while improving signal quality. This method typically involves decomposing signals using wavelet transforms, applying thresholding to wavelet coefficients, and reconstructing the denoised signal. Key MATLAB functions for implementation include wden for automated denoising, wthresh for threshold application, and wavedec/waverec for wavelet decomposition/reconstruction. In addition to wavelet threshold denoising, other common signal processing methods such as energy spectrum analysis are valuable for understanding signal characteristics. Energy spectrum analysis helps identify frequency components and power distribution within signals using techniques like periodogram or pwelch functions in MATLAB. These analytical approaches provide more accurate results and better decision-making support for various applications. By employing these signal processing techniques with proper MATLAB implementation - including parameter optimization, threshold selection strategies (soft/hard thresholding), and validation methods - users can solve practical problems more effectively. The integration of these methods enhances work efficiency and accuracy in fields such as biomedical engineering, communications, and vibration analysis. Proper code implementation should include noise estimation, level-dependent threshold calculation, and performance evaluation metrics like SNR improvement.