Gabor Filter

Gabor filters, which analyze image textures by decomposing them into frequency and orientation components, are a fundamental tool in image processing and computer vision. Current research focuses on integrating Gabor filters into various deep learning architectures, such as convolutional neural networks and transformers, to improve feature extraction for tasks like image segmentation, object recognition, and medical image analysis. This renewed interest stems from Gabor filters' ability to capture both local and global image information efficiently, leading to improved accuracy and robustness in diverse applications, including biomedical imaging, remote sensing, and robotics. The effectiveness of Gabor filters in enhancing existing models highlights their enduring relevance in modern image analysis.