Nearest Neighbor Method in Resampling

This program implements three resampling methods: Nearest Neighbor, Bilinear Interpolation, and Cubic Convolution. The Nearest Neighbor method is the simplest resampling approach, where the new pixel value is determined by selecting the value of the closest original pixel to the target location. In code implementation, this typically involves rounding the coordinates to the nearest integer index. Bilinear Interpolation provides higher accuracy than Nearest Neighbor by calculating the new pixel value based on a weighted average of the four surrounding original pixel values. The algorithm computes interpolations along both horizontal and vertical directions using linear weighting based on distance ratios. Cubic Convolution is the most complex resampling method, which calculates new pixel values by convolving 16 surrounding original pixels using cubic polynomial weighting functions. This method offers superior image quality but requires more computational resources, implemented through bicubic interpolation algorithms that consider a 4x4 pixel neighborhood. These resampling methods are widely used in image processing and computer vision applications to modify image resolution or dimensions. Each method represents a trade-off between computational efficiency and output quality, with Nearest Neighbor being fastest but potentially producing aliasing artifacts, while Cubic Convolution delivers the smoothest results at higher computational cost.