Single-Level Wavelet Decomposition Using Discrete Wavelet Transform

This program implements a single-level wavelet decomposition algorithm based on discrete wavelet transform (DWT). Discrete wavelet transform is a fundamental technique widely used in signal and image processing, capable of decomposing signals or images into different frequency subbands for detailed analysis and processing. The implementation involves key steps including signal extension, convolution with wavelet filters (using both high-pass and low-pass filters), and downsampling operations. The algorithm generates approximation coefficients (low-frequency components) and detail coefficients (high-frequency components) through filter bank processing. I personally developed this complete algorithm and created corresponding visualization results to demonstrate its effectiveness. By employing single-level wavelet decomposition through DWT, we can better understand and process characteristic features and detailed information within signals or images. The implementation includes proper handling of boundary conditions and optimal filter selection for different applications. This program is designed to provide practical understanding of wavelet decomposition principles and their implementation challenges. I hope this program proves helpful for your signal or image processing tasks.