Four-Neighborhood Weighted Average Algorithm for High-Quality Image Resampling

The Four-Neighborhood Weighted Average Algorithm is an image processing technique that achieves high-quality image resampling. This algorithm modifies pixel values by calculating weighted averages of the four adjacent pixels surrounding each target pixel (typically using the top, bottom, left, and right neighboring pixels). In practical implementation, the algorithm processes each pixel by applying specific weight coefficients to its four immediate neighbors. Common weighting approaches include equal distribution (0.25 each) or distance-based weights, where closer pixels receive higher weights. The core computation can be implemented using convolution operations with a 3x3 kernel where the center pixel weight is zero and the four direct neighbors share the total weight. This algorithm finds extensive applications in image processing for operations such as image scaling (enlargement and reduction), geometric transformations, and resolution adjustments. The key advantage lies in its computational efficiency while maintaining edge preservation better than simple averaging methods. By employing the Four-Neighborhood Weighted Average Algorithm, developers can achieve clearer and more refined image results with reduced aliasing artifacts, significantly enhancing both image quality and visual perception. The method serves as a fundamental building block for more advanced interpolation techniques in computer vision applications.