Image Interpolation Algorithms

Image interpolation algorithms are highly practical tools that can be implemented through nearest neighbor and bilinear methods. In digital image processing, interpolation serves as a fundamental technique for increasing image resolution or modifying image dimensions by estimating missing pixel values. The nearest neighbor interpolation algorithm provides a straightforward yet efficient approach by assigning the value of the closest known pixel to the target pixel position - typically implemented through coordinate rounding and direct value assignment in code. Bilinear interpolation offers higher precision by calculating a weighted average of the four surrounding known pixels, requiring implementations that handle horizontal and vertical interpolation passes with proper weighting coefficients. When implementing these algorithms in practice, key considerations include boundary handling, interpolation kernel selection, and computational efficiency optimization. Therefore, based on specific image processing requirements, developers can select appropriate interpolation algorithms to achieve desired outcomes while balancing performance and quality trade-offs.