Crack Extraction from Images using High-Frequency Techniques

This document introduces a method for crack extraction from images using high-frequency component analysis. The technique employs frequency-domain processing to separate cracks from the background by targeting high-frequency image features that typically correspond to fine details like crack patterns. The implementation typically involves applying Fourier Transform or wavelet decomposition to convert the image to frequency domain, followed by high-pass filtering (using filters like Butterworth or Gaussian high-pass) to isolate high-frequency components. The inverse transform then reconstructs the enhanced crack features while suppressing low-frequency background elements. This approach not only effectively separates cracks from background textures but also preserves crack details such as morphology, dimensions, and precise locations. The method enables comprehensive crack analysis for detection and diagnosis applications, with additional potential implementations including thresholding and morphological operations for post-processing. The technique finds broad applications beyond material inspection, including medical imaging (e.g., vessel segmentation) and geological exploration (e.g., fracture detection in rock samples), demonstrating significant practical value across multiple domains.