MATLAB Implementation of Gabor Filters

Gabor filters represent a fundamental image processing technique that performs 2D Gabor wavelet transform on images. This transform is primarily employed for feature extraction, enabling the isolation of specific information or patterns from images. Through filtering operations, Gabor filters enhance texture characteristics, edge details, and frequency information within images, thereby improving image quality and clarity. The implementation typically involves creating Gabor kernels with adjustable parameters including wavelength (frequency), orientation, phase offset, and standard deviations. In MATLAB, this can be achieved using built-in functions or custom implementations that calculate the complex Gabor function across spatial domains. The algorithm applies these kernels to images through convolution operations, producing both magnitude and phase responses that capture localized frequency content. This technology finds extensive applications in computer vision, image processing, and pattern recognition domains. Utilizing Gabor filters for image analysis facilitates better understanding and interpretation of visual data, leading to more accurate and meaningful results in applications like texture classification, edge detection, and biometric recognition systems.