MATLAB Implementation of Image Edge Detection Through Wavelet Transform

By applying wavelet transform methodology, edge information from images can be extracted through the gradient values of wavelet coefficients. This approach leverages the unique characteristics of wavelet transforms for image processing, enabling more precise identification and analysis of image edges. Through gradient operations performed on wavelet coefficients, both edge strength and directional information can be obtained, facilitating accurate edge extraction from images. From an implementation perspective, this process typically involves: - Applying multi-level wavelet decomposition using functions like wavedec2() to obtain approximation and detail coefficients - Calculating gradients from the horizontal (HL), vertical (LH), and diagonal (HH) detail coefficients - Implementing edge detection algorithms that combine magnitude and orientation information from these gradients - Using wavelet families such as Daubechies or Symlets for optimal edge detection performance As an effective image processing technique, wavelet transform has found extensive applications across numerous domains and has demonstrated significant results in edge detection applications. Key advantages include multi-resolution analysis capabilities and improved noise robustness compared to traditional edge detection methods.