Implementation of Bilinear Interpolation Algorithm

This article discusses the implementation of the bilinear interpolation algorithm, a fundamental technique widely used in digital image processing. The algorithm employs linear interpolation in two orthogonal directions to estimate the values of unknown pixels. Essentially, it functions as a local area-based image upscaling method that can be applied during image magnification operations. One significant advantage of interpolation methods is their ability to increase image dimensions without distorting the original resolution. However, careful consideration is required when applying interpolation algorithms, as they may sometimes introduce unwanted artifacts or noise during image processing. Therefore, appropriate interpolation methods must be selectively chosen based on specific use cases, followed by proper post-processing techniques. From an implementation perspective, the algorithm typically involves these key steps: 1. Identifying the four nearest neighboring pixels surrounding the target coordinate 2. Calculating horizontal interpolation weights based on relative distances 3. Performing vertical interpolation using the horizontally interpolated values 4. Normalizing results to maintain pixel value boundaries Common implementation approaches involve using nested loops for coordinate mapping or vectorized operations for better performance. The core mathematical foundation relies on weighted averages of adjacent pixel values, where weights are determined by proportional distances to the surrounding grid points.