Image interpolation techniques often are required in electronic imaging for image generation as well as processing methods such as compression, warping, or re-sampling. Since the ideal interpolation function spatially is unlimited, several interpolation kernels of finite size have been published. This tutorial introduces truncated and windowed sinc, nearest neighbor, linear, quadratic, and cubic polynomials, Gaussian, and cubic B-spline interpolation and approximation methods with kernel sizes from 1x1 up to 8x8. The numerous techniques are compared by spatial and Fourier analyses, computational complexity as well as runtime evaluation, and qualitative and quantitative interpolation error determinations for particular interpolation tasks, which were taken from common situations in electronic imaging. It is shown that superior techniques are large-sized DC-constant interpolators.