MATLAB Implementation and Application of 2D Interpolation

This article discusses the applications of 2D interpolation, primarily in image processing and data visualization. Major interpolation methods include nearest-neighbor interpolation, bilinear interpolation, and cubic spline interpolation. The source code demonstrates key implementation differences, such as how nearest-neighbor uses round() for coordinate mapping while bilinear employs weighted averaging of four adjacent pixels. Beyond core applications, 2D interpolation extends to geographic information systems (GIS) and machine learning data imputation. Method selection depends on specific application requirements and data characteristics - nearest-neighbor for speed, bilinear for smoothness, and cubic spline for high accuracy. When implementing in MATLAB, consider using griddedInterpolant for optimized performance with structured data. Proper method selection ensures accurate and reliable results while balancing computational efficiency.