Two-Level Wavelet Decomposition of Facial Images

In this project, we implemented two-level wavelet decomposition of facial images using MATLAB and obtained corresponding decomposition diagrams. Two-level wavelet decomposition is a common signal processing technique that decomposes signals into multiple sub-signals to extract more detailed information. When applied to facial images, this method helps reveal intricate details and features through hierarchical decomposition.

The implementation primarily utilizes MATLAB's wavelet toolbox functions, where wavedec2() performs the 2D wavelet decomposition and waverec2() handles reconstruction. The decomposition process generates approximation coefficients (low-frequency components) and detailed coefficients (horizontal, vertical, and diagonal high-frequency components) at each level. Notably, this approach requires minimal technical background - users need only modify the input image filename in the code to apply it to their own images. For those unfamiliar with wavelet decomposition, we recommend consulting relevant documentation to understand the mathematical principles behind multi-resolution analysis. Overall, this method serves as a valuable tool for enhanced image understanding and analysis, particularly useful for feature extraction in facial image processing applications.