Image Segmentation Method Using Wavelet Transform with MATLAB Implementation

This article presents an image segmentation method based on wavelet transform, accompanied by complete MATLAB implementation code. Wavelet transform serves as a powerful mathematical tool for signal and image processing, capable of decomposing signals or images into frequency components at different scales for enhanced analysis and processing. Our methodology utilizes wavelet decomposition to break down images into regions of varying sizes, effectively achieving image segmentation. The MATLAB code demonstrates practical implementation using key functions such as wavedec2 for 2D wavelet decomposition and watershed transform for region boundary detection. The algorithm involves three main steps: multi-scale decomposition using Daubechies wavelets, coefficient thresholding for feature enhancement, and inverse transformation for segmented image reconstruction. This wavelet-based segmentation approach provides researchers with a reliable tool for image processing applications, promising more accurate and robust image analysis results for various computer vision tasks. We believe this method will become an essential technique in the image processing domain, particularly for applications requiring precise boundary detection and multi-resolution analysis.