Orthonormal Dictionaries for Band Limited Signals
Why would you add the constraint of being Orthonormal Dictionary?
It doesn't make sense in the context of what you ask.

First we need to define resolution.
If you mean the grid to be denser than indeed what you need is to create the DFT matrix of zero padded signal and take the subset which you're interested in as you wrote in your question.

If you define resolution as the ability to discern between 2 close (In frequency relative to the observation window) harmonic signals then there is a subtle thing to keep in mind.
While indeed by Uncertainty Principle you're limited by the observation time what's important is under what model.
If you have no knowledge about the signal but it is band limited than this holds and probably there is nothing to do.  

Yet if you have some prior on it you can do something and indeed gain some ability to "Bypass" the limitation.

For instance, if you assume the signal is sparse relative to the DFT matrix than solving the problem with $ {L}_{1} $ regularization (Sparse Model / MAP Model with Laplacian Prior) will yield ability to have "Super Resolution".

I can't, currently, think on a prior to work on range of resolutions, but if you believe your signal is sparse in that range you certainly can build an optimization problem to gain extra resolution.