Calculating Signal-to-Noise Ratio After Denoising

In this document, we will collectively explore the signal-to-noise ratio (SNR) of signals after denoising processing. First, we need to understand what SNR represents. SNR is a key metric for evaluating signal quality, defined as the ratio of signal power to noise power. Higher SNR values indicate better signal quality. Next, we will discuss denoising techniques to improve SNR. Denoising processing refers to computational methods that reduce noise components in signals while preserving useful information. We will introduce commonly used denoising algorithms including mean filtering (implemented using sliding window averaging), median filtering (effective for impulse noise removal), and wavelet denoising (utilizing multi-resolution analysis). Finally, we will demonstrate how to calculate SNR after denoising using the formula SNR = 10*log10(Ps/Pn), where Ps represents signal power and Pn denotes noise power, to objectively evaluate denoising effectiveness. We encourage active participation in discussions and welcome sharing of practical implementation experiences and code snippets.