Image Edge Detection Using Wavelet Transform with MATLAB Implementation

This document presents a method for image edge detection using wavelet transform, implemented in MATLAB through the edgedetect_basedonWavelet.m file. The approach enables more accurate detection of image edges and provides a reliable tool for image data processing. By utilizing wavelet transform, we can analyze the frequency characteristics of images and identify edges based on these features. The implementation typically involves decomposing images using wavelet filters (like Haar or Daubechies), processing detail coefficients at multiple scales, and applying thresholding techniques to enhance edge components. This method is particularly valuable in image processing applications and can be effectively applied across various domains including computer vision and medical image analysis. The edgedetect_basedonWavelet.m file constitutes a complete implementation containing all necessary functions and algorithms, ready to execute directly in the MATLAB environment. Key algorithmic components include wavelet decomposition using functions like wavedec2, coefficient thresholding for noise reduction, and edge reconstruction from processed wavelet coefficients. We encourage users to experiment with this method and make appropriate adjustments according to their specific requirements. By understanding and mastering this technique, you will gain enhanced capabilities for processing and analyzing image data, leading to improved results across various applications. The code structure follows modular design principles, separating wavelet transformation, coefficient processing, and edge visualization into distinct functional sections for better maintainability and customization.