MATLAB Code for Wavelet Denoising Implementation

This text discusses wavelet denoising, a signal processing technique used for noise removal from signals. The method operates by decomposing the input signal into different frequency wavelet components using wavelet transform functions like wavedec() in MATLAB. Each wavelet component undergoes threshold processing where coefficients below a specified threshold (determined by algorithms such as Universal Threshold or Minimax) are eliminated or reduced. The thresholding can be implemented using functions like wthresh() with hard or soft thresholding approaches. Finally, the processed wavelet components are reconstructed into the denoised signal using waverec() or similar reconstruction functions. Wavelet denoising finds applications across multiple domains including audio processing (where it can clean audio signals using appropriate wavelet families like Daubechies), image processing (for noise reduction in digital images using 2D wavelet transforms), biomedical signal analysis, and communication systems. The effectiveness depends on proper selection of wavelet type, decomposition level, and thresholding strategy.