MATLAB Implementation of Wavelet Decomposition and Reconstruction

Wavelet decomposition and reconstruction techniques enable effective image enhancement and fusion applications. This MATLAB implementation allows flexible selection of various wavelet types (such as Daubechies, Haar, or Coiflets) through the wavedec2 and waverec2 functions. The decomposition process involves breaking down images into approximation and detail coefficients across multiple resolution levels using discrete wavelet transform (DWT). For image enhancement, modified coefficients are reconstructed through inverse DWT, while fusion algorithms combine coefficients from multiple images based on selection rules. Beginners can easily adapt the code by modifying wavelet parameters in the 'wname' variable and adjusting threshold values for coefficient processing. Beyond image processing, these wavelet techniques find applications in signal processing, data compression, and feature extraction. Mastering this implementation provides fundamental skills for multiresolution analysis and improves performance in various digital signal processing domains.