Implementation of Bicubic Linear Interpolation Function in Digital Image Processing

This function implements a widely used interpolation method in digital image processing known as bicubic linear interpolation. The primary purpose of this function is to perform image scaling (enlargement and reduction) by calculating new pixel values through interpolation computations. The algorithm works by considering the values of surrounding 4x4 pixels to compute each new pixel value, using cubic polynomials for both horizontal and vertical directions. This approach achieves smooth image transitions while preserving fine details effectively. The implementation typically involves calculating 16 neighboring pixel weights using bicubic convolution kernels, then applying weighted averaging to generate interpolated values. Since this method maintains image clarity and detail integrity during scaling operations, it has become extensively adopted in digital image processing applications. Key implementation aspects include handling edge cases with symmetric padding, optimizing computation through separable convolution, and managing color channel interpolation for RGB images.