Wavelet Transform of Signals with Hard Threshold Filtering

In this document, we perform wavelet transform on signals. Through this transformation, we achieve coefficient suppression via wavelet filtering. We implement the hard thresholding method for filtering. This approach effectively processes signals by removing noise components and enhancing signal quality. The hard thresholding technique works by setting wavelet coefficients below a specific threshold to zero while preserving coefficients above the threshold unchanged. This selective coefficient processing enables efficient noise reduction while maintaining important signal features. Commonly implemented using functions like wdenoise() or wavedec() in signal processing libraries, the method involves decomposing signals into approximation and detail coefficients, applying thresholding to detail coefficients, and reconstructing the signal. Therefore, we conclude that by applying wavelet transform combined with hard threshold filtering, we achieve superior results in signal denoising applications.