While I loved the topic of my PhD, I had to take a break after staring at that problem for several years. Thereafter, I was side-tracked by several other projects, and never got around to publishing a paper on my dissertation.
Here, then, is a summary of the simple but effective super-resolution algorithm described therein:
I also submitted this work to NIPS: the reviewers liked the paper, but they were not convinced of its novelty. Having spent a lot of time studying the existing literature, all I can say in response is that, while solving the problem as a sparse linear system was well known at the time, phrasing Drizzle as a linear operator and using it for super-resolution was not.
But the proof of the pudding is in the eating! Have a look at the results and published code – you can download it all (including a sample data-set) and play with the different reconstruction parameters. Quite a bit of the code has since graduated into scikit-image.