Image Edge Detection with Wavelet Multiscale Analysis

This article introduces image edge detection implementation using MATLAB. Edge detection serves as a critical step in image processing, enabling the identification and segmentation of distinct regions within images. The wavelet multiscale edge detection method, which detects edges at multiple scales, represents a commonly adopted approach. Our implementation includes the main program for wavelet multiscale edge detection, detailing how to synthesize vertical and horizontal detail coefficients and compute modulus maxima. The process involves applying wavelet decomposition to extract detail coefficients at different scales, followed by modulus calculation using sqrt(v² + h²) and direction determination through arctan(h/v). To enhance detection accuracy, we implement adaptive thresholding techniques that dynamically adjust threshold values based on local image characteristics. These methods utilize statistical properties of the modulus matrix, such as employing the Otsu method or percentile-based thresholds. The combined techniques provide robust edge detection capabilities, allowing users to extract more meaningful information from images through systematic multiscale analysis.