Multi-scale Discrete Wavelet Decomposition for 2D Signals
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When processing signals, we can employ multi-scale discrete wavelet decomposition methodology. This approach decomposes signals into different frequency components, enabling better understanding of signal characteristics. The implementation utilizes wavelet transform algorithms (such as Daubechies or Haar wavelets) through functions like wavedec2() for decomposition and wrcoef2() for coefficient reconstruction. By executing the program, we can successfully visualize the effects at different frequency bands after wavelet decomposition, which facilitates further analysis and processing of signal data. The code typically includes steps for multiple-level decomposition, coefficient extraction, and subband visualization using image display functions.
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