Interpolation is the process of estimating the values of a continuous function from discrete samples.

Image processing applications of interpolation include image magnification or reduction, subpixel image registration, to correct spatial distortions, and image decompression, as well as others. Of the many image interpolation techniques available, nearest neighbour, bilinear and cubic convolution are the most common.

There are a number of techniques that can be used to enlarge an image. Implement three Interpolation methods (such as Lagrangian Interpolation, Cubic spline, bilinear, bicubic etc.) for three different images and compare the results.

Step-1: Read Image

I = imread('cameraman.tif');

figure;imshow(I); title('Original Image');

Step-2: Simulate three Interpolation Methods

Increase the dimensions of the image from 720x480 (typical resolution in DVD frames) to 1920x1080 (typical resolution in high definition HD video frames) using numerical integration methods such as Lagrnagian interpolation, cubic spline, bilinear interpolation, etc.

Step-3: Analyzing

There is a trade-off between computational complexity and accuracy. Compare your methods in terms of computational complexity and accuracy.