Self-Developed Image Interpolation Algorithms

This collection features my custom implementations of essential image interpolation algorithms designed for practical applications. The suite includes three core interpolation methods: Nearest-neighbor interpolation provides a computationally efficient approach by directly assigning the value of the closest input pixel to each output coordinate. The algorithm implementation typically involves simple coordinate mapping and rounding operations, making it ideal for real-time applications where speed outweighs quality concerns. Bilinear interpolation employs a more sophisticated technique that calculates output pixel values through weighted averaging of the four nearest input pixels. The implementation uses linear interpolation first in one direction, then perpendicularly, requiring careful handling of pixel boundaries and floating-point coordinate calculations for improved accuracy over nearest-neighbor methods. Bicubic interpolation represents a premium-quality approach utilizing 16 surrounding pixels to generate smoother results with reduced artifacts. The algorithm implementation involves cubic convolution kernels that consider pixel intensity gradients, requiring more complex mathematical operations but delivering superior image quality suitable for professional image scaling applications. These algorithm implementations feature optimized memory management and boundary handling, providing robust solutions for various image processing scenarios where controlled interpolation quality is required. Each method includes parameter tuning capabilities to balance between computational efficiency and output quality based on specific application needs.