The super-resolution reconstruction process deals with the fusion of several low quality and low-resolution images into one higher-resolution and possibly better final image. We start by showing that from theoretic point of view, this fusion process is based on generalized sampling theorems due to Yen (1956) and Papulis (1977). When more realistic scenario is considered with blur, arbitrary motion, and additive noise, an estimation approach is considered instead.
We describe methods based on the Maximum-Likelihood (ML), Maximum-A-posteriori Probability (MAP), and the Projection onto Convex Sets POCS) as candidate tools to use. Underlying all these methods is the development of a model describing the relation between the measurements (low-quality images) and the desired output (high-resolution image). Through this path we presents the basic rational behind super-resolution, and then present the dichotomy between the static and the dynamic super-resolution process. We proposed treatment of both, and deal with several interesting special cases.