MATLAB Implementation of Gabor Wavelet Filter

This article presents a highly practical tool - a MATLAB-implemented Gabor wavelet filter that supports both one-dimensional and two-dimensional signal processing. The implementation includes functions for filtering signals and images to extract relevant features through time-frequency decomposition. Wavelet filters are powerful tools for analyzing signals in both time and frequency domains simultaneously. The algorithm works by performing wavelet transformation on input signals, decomposing them into wavelet coefficients at different frequency bands and time scales. This multi-resolution analysis enables detailed signal examination and processing. The Gabor wavelet filter implementation features a Gaussian window function in the time domain, allowing localized analysis of signal characteristics. The MATLAB code includes key functions for generating Gabor filter banks with customizable parameters such as center frequencies, bandwidths, and orientations (for 2D cases). The implementation efficiently handles both real and imaginary components of Gabor wavelets, providing complete quadrature filter pairs for optimal feature extraction. For 2D image processing, the code contains specialized functions that apply Gabor filters at multiple orientations and scales, enabling texture analysis, edge detection, and pattern recognition. The 1D version includes optimized convolution algorithms for efficient signal filtering and feature extraction from time-series data. This tool is particularly valuable in signal and image processing research, offering researchers comprehensive analysis capabilities through its robust implementation of Gabor wavelet theory with practical MATLAB programming techniques.