Image Filtering Using Log-Gabor Filters

In image filtering programs, utilizing log-Gabor filters for filtering operations is a common approach. These filters effectively extract detailed features and texture information from images, thereby enhancing image quality. By adjusting filter parameters such as center frequency, bandwidth, and orientation scales, developers can achieve varying degrees of image enhancement and noise reduction effects. Typically implemented through frequency-domain convolution using FFT, the algorithm involves designing log-Gabor transfer functions in the Fourier domain and applying inverse transforms. Key functions include bandwidth control for scale selection and multi-orientation processing for texture analysis. Consequently, in image processing and computer vision fields, log-Gabor filters are widely applied in various scenarios including object detection, image recognition, and facial recognition systems due to their biological relevance and optimal spatial localization properties.