Wavelet Multi-Scale Grayscale Image Texture Feature Detection Source Code

In wavelet multi-scale grayscale image texture feature detection, the core approach leverages the multi-scale characteristics of wavelet transform to extract texture information from images. Wavelet transform decomposes images into different frequency bands, capturing texture details at various resolutions. First, wavelet transform decomposes the image into sub-bands at different scales, including low-frequency approximation components and high-frequency detail components (horizontal, vertical, and diagonal directions). The low-frequency components preserve the main structure of the image, while high-frequency components contain edge and texture details. In code implementation, this is typically achieved using functions like wavedec2() in MATLAB or pywt.wavedec2() in Python's PyWavelets library. By customizing wavelet basis functions, appropriate wavelet types can be selected based on specific requirements, such as Haar, Daubechies, or Symlets wavelets. In programming practice, this involves specifying the wavelet family and order parameters in the decomposition function. Different wavelet bases have varying sensitivity to texture features, and selecting the optimal basis function can enhance feature extraction performance through parameter tuning. During the feature extraction phase, statistical properties of each sub-band are typically calculated, such as energy, entropy, contrast, and homogeneity, to form multi-scale texture feature vectors. Code implementation would involve computing these metrics using functions like mean(), std(), or custom statistical calculations on the wavelet coefficients. These features can be utilized for subsequent classification or recognition tasks, such as medical image analysis or industrial inspection applications. The key advantage of this method lies in the multi-scale nature of wavelet transform, which simultaneously captures both global and local texture features of images, making it suitable for complex grayscale image analysis scenarios. The algorithm's implementation typically includes parameters for decomposition levels and feature selection strategies to optimize performance for specific application domains.