Wavelet Threshold Denoising Routine

The wavelet threshold denoising routine is a signal processing technique designed to reduce noise in various signals. This method can be applied to multiple types of source signals including audio signals, image signals, and video signals. During implementation, developers can choose from various thresholding approaches such as fixed thresholds, adaptive thresholds, and soft/hard thresholding methods. The core algorithm typically involves: 1) performing wavelet decomposition using functions like wavedec() to obtain coefficients, 2) applying thresholding operations using wthresh() with selected parameters, and 3) reconstructing the signal via waverec(). By selecting appropriate threshold types and parameters, this technique effectively reduces noise interference while preserving critical signal characteristics. Key implementation considerations include determining decomposition levels, threshold calculation methods (e.g., universal threshold or minimax threshold), and threshold application rules. Consequently, wavelet threshold denoising serves as a valuable signal processing technique applicable to various practical scenarios including speech enhancement, image denoising, and video compression, with MATLAB's Wavelet Toolbox providing built-in functions like wden() for streamlined implementation.