Image Super-Resolution - Iterative Back Projection Algorithm

A simple maximum-likelihood algorithm for super-resolution.

This project is a simple implementation of the Iterative Back-Projection (IBP)
algorithm for solving the Super-Resolution problem. It was first proposed
by Michal Irani in her 1991 paper "Improving resolution by image
registration". The imaging model being used is described by a paper by
Michael Elad, "Super-Resolution Reconstruction of an image". Both papers
can easily be found through a search in Google Scholar.

I've done two simplifications to the imaging model:
1) The image blur is assumed to be spatially invariant.
2) The spatial transformation model is a global translation.

To run the example code, follow the following steps:
1) Run SRSetup.m
2) Run SRExample.m

The example code operates on a dataset that is generated synthetically from
a reference image. Thus, the exact values for the blur sigma and the
translation offsets are being used.