Abstract
This chapter presents solutions for the image interpolation problem developed in a general framework based on dealing with image interpolation as an inverse problem rather than a signal synthesis problem. An inverse problem is characterized as ill-posed when there is no guarantee for the existence, uniqueness, and stability of the solution based on direct inversion. When the image acquisition model is non-ideal, which is our case, a correction filter is required prior to interpolation to compensate for the non-ideality of the image acquisition model. A mathematical model for image interpolation has been derived based on the maximization of the a priori entropy of the high-resolution image. The adaptive least-squares image interpolation algorithm has also been tested on the same low-resolution image. The linear minimum mean square error image interpolation algorithm has been tested for differ.