MATLAB Simulation of Wavelet Multi-Resolution Denoising and Curve Fitting Denoising

Through MATLAB simulations, we can implement both wavelet multi-resolution denoising and curve fitting denoising techniques. Wavelet multi-resolution denoising is an effective signal processing method that removes noise from signals while preserving important signal characteristics using wavelet decomposition and thresholding algorithms. This involves applying wavelet transforms like 'db4' or 'sym8', decomposing signals into multiple resolution levels, and applying thresholding functions such as 'wden' or 'wthresh' to eliminate noise components. Curve fitting denoising is another widely used data processing approach that reduces noise by fitting smooth curves to data points using polynomial fitting, spline interpolation, or least-squares methods. These techniques are extensively applied in signal processing and data analysis fields, significantly improving data quality and accuracy. MATLAB implementations typically utilize functions like 'wden' for wavelet denoising and 'polyfit' or 'spline' for curve fitting operations.