Simplification and Performance Enhancement of Image Processing via Hexagonal Grids

This research explores the simplification and performance enhancement of image processing algorithms through the adoption of hexagonal grid representation. Unlike conventional square pixel structures, hexagonal grids offer superior sampling efficiency, uniform connectivity, and reduced redundancy in neighborhood operations. By leveraging these properties, the study demonstrates how computational complexity can be minimized while improving accuracy in edge detection, filtering, and segmentation tasks. Sobel edge detection is effective for highlighting strong, linear boundaries but is limited by its sensitivity to noise and inability to capture finer textures. In contrast, the Canny method delivers cleaner, thinner, and more continuous edges by integrating smoothing and thresholding techniques, making it more robust and precise. Simulation results highlight that hexagonal representation enhances visual quality, reduces processing time, and provides a more natural approximation of circular features, making it a promising alternative for advanced image processing applications.