MATLAB Implementation and Comparison of Bilinear Interpolation and Nearest Neighbor Interpolation

This article presents the fundamental principles of bilinear interpolation and nearest neighbor interpolation, accompanied by MATLAB source code to facilitate deeper investigation into both algorithms' performance. We begin with a detailed explanation of the bilinear interpolation algorithm's mechanism, which utilizes weighted averaging of four neighboring pixels to achieve image scaling operations. The provided MATLAB implementation demonstrates how to calculate intermediate pixel values through coordinate mapping and linear interpolation in both horizontal and vertical directions using functions like meshgrid and interp2.

Subsequently, we introduce the nearest neighbor interpolation approach, where each interpolated pixel directly adopts the value of its closest original pixel through simple rounding operations. The corresponding MATLAB code illustrates efficient implementation using array indexing and coordinate transformation techniques, highlighting the algorithm's computational simplicity through functions such as round and basic matrix operations.

The comparative analysis section evaluates both methods in terms of computational complexity, output quality, and practical applications. Bilinear interpolation produces smoother results with reduced aliasing artifacts but requires more processing resources, while nearest neighbor interpolation maintains sharper edges with faster execution but may introduce blockiness. Through practical MATLAB examples and performance metrics, readers will gain comprehensive understanding of these fundamental image processing techniques and their appropriate implementation scenarios in digital image manipulation.