FPGA Implementation of Edge Detection Algorithms for Image Processing

In the field of image processing, edge detection plays a crucial role in extracting meaningful structural information from visual data. It is widely used in applications such as object recognition, feature extraction, and motion analysis in computer vision. Among various edge detection techniques, the Canny edge detector is recognized for its optimal performance in detecting true edges while minimizing false detections. This work presents a comparative study of edge detection methods with a special focus on the Canny algorithm, highlighting its efficiency and precision over conventional techniques. To enhance its performance on blurred and noisy images, a Median filter is employed as a preprocessing step. The Median filter effectively reduces noise while preserving edges, resulting in a more accurate and robust edge detection pipeline. The proposed improvement demonstrates superior edge preservation in challenging visual conditions, validating the effectiveness of integrating edge-preserving noise reduction with traditional Canny edge detection.