MATLAB Implementation of Image Wavelet Transform

Image wavelet transform is a mathematical technique for decomposing and reconstructing images. It breaks down images into wavelet coefficient maps at different frequencies, revealing detailed and structural information within the image. The decomposition level (order) can be customized according to requirements, enabling varying degrees of image decomposition and reconstruction. In MATLAB implementation, this typically involves using functions like wavedec2 for 2D discrete wavelet decomposition, where users can specify the wavelet type (e.g., 'db1', 'haar') and decomposition level. The algorithm works by applying high-pass and low-pass filters to both rows and columns, generating approximation coefficients (low-frequency components) and detail coefficients (horizontal, vertical, and diagonal high-frequency components). This method finds extensive applications in image processing and analysis, including image compression (by thresholding small coefficients), edge detection (using high-frequency components), and image enhancement (through coefficient manipulation). Key MATLAB functions include: wavedec2 for decomposition, waverec2 for reconstruction, and dwt2/idwt2 for single-level transformations. The implementation allows visualization of coefficient maps using wcodemat for matrix coding and scaling.