Wavelet Edge Detection Algorithm Implementation for Image Edge Detection

In the field of image processing, the wavelet edge detection algorithm can effectively perform edge detection on images. This algorithm leverages the characteristics of wavelet transform to capture edge information in images, thereby enhancing image clarity and detail representation. The wavelet edge detection algorithm is a multi-scale analysis-based method that acquires edge features at different scales through multiple decomposition and reconstruction operations on the image. This approach not only detects strong edges in images but also enhances weak edges, improving overall edge detection performance. Implementation typically involves applying wavelet transforms (using functions like wavedec2 in MATLAB) to decompose images into approximation and detail coefficients. Edge detection is performed by analyzing high-frequency components across scales, often applying thresholding techniques to detail coefficients. The algorithm can be implemented using wavelet filters (e.g., Daubechies or Symlets) and may involve modulus maxima detection for precise edge localization. Reconstruction techniques like wavelet inverse transforms (waverec2) help preserve edge details while reducing noise. Therefore, using the wavelet edge detection algorithm for image edge detection proves to be an effective method with robust multi-scale analysis capabilities.