Wavelet-Based Edge Detection Using Biorthogonal Wavelets

This MATLAB program implements edge detection using wavelet transformation. The implementation utilizes biorthogonal wavelets (bior wavelets) to perform multi-scale edge analysis on images, achieving excellent detection performance. Wavelet transform is a signal processing technique that decomposes signals into different frequency subbands, enabling precise identification of edges in images. The program works by: 1. Applying 2D discrete wavelet transform (DWT) using biorthogonal wavelet filters 2. Analyzing wavelet coefficients across multiple decomposition levels 3. Implementing thresholding techniques to enhance edge detection accuracy Key MATLAB functions employed include: - wavedec2() for 2D wavelet decomposition - wthresh() for coefficient thresholding - edge() or custom edge detection algorithms - biorwavf() for biorthogonal wavelet filter generation By leveraging wavelet transformation for edge detection, the program accurately captures fine details and edge information in images, significantly improving image processing outcomes. This wavelet-based approach proves particularly effective for handling images with complex textures and varying edge characteristics, making it a robust solution for advanced image analysis applications.