Wavelet Modulus Maxima for Image Edge Detection and Extraction

This paper presents a program that utilizes wavelet modulus maxima for detecting and extracting image edges. This method proves highly effective as it accurately captures edge details through multiscale wavelet decomposition while preserving image clarity and precision during extraction. The algorithm implementation typically involves: 1) Performing 2D discrete wavelet transformation to obtain horizontal, vertical, and diagonal detail coefficients; 2) Calculating modulus magnitudes and gradient directions at each scale; 3) Identifying local maxima points along gradient directions to suppress non-edge responses. One key advantage lies in its applicability to diverse image types including natural landscapes, portraits, and abstract artworks. Furthermore, the program demonstrates robust stability by incorporating noise-thresholding mechanisms and adaptive filtering, enabling reliable performance under varying illumination conditions and image noise levels. Consequently, this approach offers broad application prospects in image processing domains, providing researchers and engineers with a powerful tool for edge detection tasks. The core functions may include wavelet basis selection, multiscale threshold optimization, and edge-linking algorithms for continuous boundary extraction.