MATLAB Implementation of Gabor Wavelet with Multi-Scale Multi-Orientation Transformation

The Gabor wavelet program is a computational implementation designed to perform multi-scale and multi-directional transformations on images. This program utilizes Gabor filters, which are linear filters characterized by their optimal resolution in both frequency and spatial domains, making them particularly effective for texture analysis and feature extraction. The implementation typically involves creating a bank of Gabor filters with varying scales (frequencies) and orientations, achieved through parameters like wavelength (lambda), orientation (theta), and standard deviation (sigma). Key implementation aspects include generating complex Gabor kernels using mathematical formulations that combine Gaussian envelopes with sinusoidal carriers, followed by convolution operations with input images. The program can reveal subtle details and patterns in images by capturing localized frequency information across different orientations. A typical MATLAB implementation would involve functions like gaborFilterBank creation, where users can specify the number of scales and orientations, followed by image convolution using conv2 or imfilter functions. The advantage of this Gabor wavelet program lies in its flexibility to adapt to various image types and scenarios through parameter tuning. By adjusting parameters such as the aspect ratio (gamma) and bandwidth, users can optimize the filters for specific applications like edge detection, texture segmentation, or fingerprint recognition. This program serves as a valuable tool for image processing tasks, providing rich analytical results that enhance understanding and interpretation of image data patterns.