Wavelet Packet Decomposition of an Image
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Wavelet packet decomposition is a commonly used image processing technique that decomposes an image into multiple-scale image subbands. Each subband contains information at different frequency ranges. By extracting coefficients from each decomposition level, we can obtain feature information of the image at various scales, enabling more precise and accurate image processing and analysis. The implementation typically involves using functions like wpdec2() in MATLAB or pywt.wavedec2() in Python's PyWavelets library, which recursively apply high-pass and low-pass filters to create a complete wavelet packet tree structure. The decomposition process generates approximation coefficients (low-frequency components) and detail coefficients (horizontal, vertical, and diagonal high-frequency components) at each level, providing a multi-resolution representation of the original image.
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