Implementation of Wavelet Transform Applications in MATLAB

In this technical discussion, we examine various applications of wavelet transform implementations. Beyond the fundamental soft-thresholding and hard-thresholding denoising algorithms, we explore several enhancement methodologies. These improvements may incorporate alternative threshold selection strategies, such as universal thresholding or minimax thresholding approaches, and experimentation with different wavelet basis functions including Daubechies, Symlets, or Coiflets wavelets. Through MATLAB implementation, these enhanced methods typically involve modifying threshold calculation functions (e.g., wthresh) and wavelet decomposition parameters (using wavedec function). By systematically evaluating these algorithmic refinements through quantitative metrics like SNR and MSE calculations, we can significantly optimize wavelet transform performance in noise reduction applications. The code structure generally follows: wavelet decomposition → threshold application → wavelet reconstruction, with modifications focusing on adaptive threshold determination and optimal wavelet family selection.