Implementation of Bicubic Interpolation Image Scaling Algorithm

In the provided implementation, we can adjust the magnification or reduction factor of images by modifying the parameter K value, thereby achieving the bicubic interpolation image scaling algorithm. This algorithm represents a fundamental technique for image resizing that employs a third-degree polynomial (usually the Catmull-Rom spline) to calculate pixel values from 16 neighboring points, ensuring optimal balance between smoothness and sharpness preservation. The core implementation involves three key computational phases: 1) Calculating horizontal interpolation coefficients using the cubic convolution kernel 2) Applying vertical interpolation with the same weighting function 3) Normalizing final pixel values through bilinear combination By adjusting the K parameter (typically controlling interpolation intensity or scaling factor), users can precisely control the image scaling ratio to meet specific requirements. The algorithm's mathematical foundation ensures minimal artifacting while maintaining edge definition, making it particularly suitable for photographic enlargement and medical imaging applications. This provides a robust and efficient method for image dimension adjustment across various technical applications, with typical implementation involving nested loops for pixel coordinate mapping and convolution operations with boundary handling mechanisms.