Custom Image Interpolation Algorithm Implementations

This repository contains self-developed implementations of commonly used image interpolation algorithms. The collection includes nearest neighbor interpolation, bilinear interpolation, and bicubic interpolation methods. Nearest neighbor interpolation represents the simplest approach where the target pixel's value is directly assigned from its closest neighboring pixel in the original image. This algorithm can be efficiently implemented using simple coordinate mapping and ceil/floor functions, though it may result in noticeable pixelation artifacts during significant scaling operations. Bilinear interpolation provides smoother results by calculating weighted averages of the four closest neighboring pixels. The implementation typically involves two sequential linear interpolations - first horizontally between pixel pairs, then vertically between the intermediate results. This method offers a good balance between computational efficiency and visual quality. Bicubic interpolation represents a more advanced technique that considers 16 surrounding pixels to generate superior image quality with better detail preservation. The algorithm employs cubic convolution kernels (often using Catmull-Rom or B-spline basis functions) to compute pixel values, requiring more complex mathematical operations but producing significantly smoother gradients and reduced artifacts. These interpolation algorithms find widespread applications in image processing and computer vision domains. Practical implementations include image upscaling/downscaling operations, image restoration projects, and real-time computer vision systems where efficient resampling is crucial. Each implementation includes optimized code structures with proper boundary handling and memory management considerations for production-ready deployment.