Image Interpolation Algorithms

This document discusses three fundamental image interpolation methods: Nearest Neighbor Interpolation, Bilinear Interpolation, and Convolution-Based Interpolation. Image interpolation is a pixel-level processing technique used to enhance image resolution or modify image dimensions. Nearest Neighbor Interpolation represents the simplest approach, where new pixel values are generated by directly copying the value of the closest existing pixel. In code implementation, this typically involves rounding coordinate calculations to select the nearest pixel index, making it computationally efficient but potentially producing jagged edges. Bilinear Interpolation offers higher precision by calculating new pixel values through weighted averaging of four adjacent pixels. The algorithm computes horizontal and vertical interpolations sequentially, requiring bi-directional linear calculations that yield smoother transitions between pixels. This method is commonly implemented using coordinate transformation matrices and linear weighting functions. Convolution-Based Interpolation employs filter kernels to generate new pixels through convolution operations. This category includes advanced methods like Bicubic and Lanczos interpolation, which apply specific kernel functions to surrounding pixels. The implementation involves sliding window operations and kernel matrix multiplication, providing superior edge preservation and minimal artifacts. These interpolation techniques are widely applied in image processing and computer vision fields for tasks including image upscaling, downscaling, and reconstruction workflows.