Comparison of Programs and Execution Results for Several Wavelet Threshold Function Denoising Methods

In this article, I will introduce several wavelet threshold function denoising programs and compare their execution results. First, we will explore the principles and algorithms behind each wavelet threshold function, including hard thresholding, soft thresholding, and improved thresholding methods. Next, we will provide detailed implementation guidelines with corresponding code examples in MATLAB or Python, demonstrating key functions such as wavelet decomposition, threshold calculation, and signal reconstruction. Then, we will execute these programs and evaluate their denoising performance using metrics like Signal-to-Noise Ratio (SNR) and Mean Squared Error (MSE), comparing their effectiveness on various test signals. Finally, I will summarize our research findings and suggest potential directions for future studies. Through this comprehensive discussion, readers will gain a deeper understanding of wavelet threshold function denoising techniques and be able to select appropriate methods for practical applications based on their specific requirements.