Image Interpolation Algorithms: Nearest Neighbor and Bilinear Interpolation with MATLAB Implementations

Image interpolation algorithms are fundamental techniques in digital image processing that estimate unknown pixel values through calculations between known data points. Among these, nearest neighbor, bilinear, and bicubic interpolation represent the three most commonly used methods in practical applications.

Nearest neighbor interpolation is a simple and computationally efficient approach that assigns the value of the closest known pixel to the target position. This method is particularly suitable for pixel-level image processing and maintains low computational complexity when handling large-scale images. In MATLAB implementation, this typically involves using the 'nearest' option in the imresize function or manually calculating coordinate mappings through rounding operations.

Bilinear interpolation employs a linear interpolation-based methodology that estimates target pixel values using four surrounding known data points. This approach generally yields more accurate results than nearest neighbor interpolation, especially in preserving image smoothness and detail representation. The MATLAB implementation commonly utilizes the 'bilinear' parameter in imresize, or can be custom-coded by calculating weighted averages based on relative distances between the target point and its four neighboring pixels.

Bicubic interpolation represents a more sophisticated technique that utilizes 16 surrounding known pixels to estimate target values. This method achieves higher interpolation accuracy, particularly excelling in high-resolution image processing and magnification scenarios. While not covered in the provided code, it's typically implemented in MATLAB using the 'bicubic' option in imresize, involving complex polynomial calculations for enhanced precision.

For researchers and developers interested in practical implementations, we provide reference MATLAB code for both nearest neighbor and bilinear interpolation algorithms. These implementations demonstrate key concepts such as coordinate transformation, weighting calculations, and boundary handling, offering valuable insights into the application principles of image interpolation algorithms.