Image Enhancement using Wavelet Transform - MATLAB Implementation Example

This example demonstrates image enhancement using wavelet transforms with MATLAB code implementation. The process begins by importing the target image and converting it to grayscale format for simplified processing. The core algorithm involves applying wavelet decomposition (using functions like wavedec2) to break down the image into multiple frequency subbands - typically approximation coefficients (LL) and detail coefficients (LH, HL, HH) representing horizontal, vertical and diagonal components. Each subband undergoes enhancement processing; for instance, histogram equalization (histeq function) is applied to improve contrast in specific frequency ranges. After modifying the wavelet coefficients, the inverse wavelet transform (waverec2 function) reconstructs the enhanced image back to the spatial domain. This workflow illustrates fundamental wavelet-based image processing concepts, including multi-resolution analysis and selective frequency amplification. Through this practical MATLAB implementation, users can learn to manipulate wavelet coefficients for targeted image improvements and understand how different subbands contribute to visual quality enhancement.