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My take is this. First, a "conventional approach" (N_f = N_t = N) is
straightforward. N samples "maps" into N frequencies and vice versa
with the inverse transform. I like your term "barely invertible."
If we then increase the resolution in frequency-space (f-space) by
increasing N_f as discussed, it might be assumed that we can achieve
a higher resolution result in the time domain when inverse
transforming back to it.
But it doesn't work like that. We can't get
back the original waveform. Rather, we might I suppose (?), but there
is additional content created in the time domain, in particular
imaginary content, whereas the original time domain contained only
real data. Not good.
Two points:
1. If we abandon trying to write the transforms as summations, and
instead move to a matrix formulation, it is easy to move back and
forth in the following sense:
F = E . T
Here, T is the vector of samples in the time domain, and F is the
vector of frequencies (meaning the vector of transformed values in
f-space, not the frequencies themselves - words fail).
E is the
square matrix, constructed of the exponential terms that appear in
the summations. For N_f = N_t = N, this is just the equation for the
discrete fourier transform, and its inverse is T = E^-1 . F, where
E^-1 is just the conventional inverse of a square matrix.
Now, increasing the f-space resolution amounts to enlarging the
number of terms in vector F, and the number of rows in E,
such that T
remains intact. We can then still write T = E^-1 . F, where E^-1 is
now the pseudoinverse of the non-square matrix E. By doing so, we
recover the original time-domain samples without any "additional
content," no matter how much we increase the resolution in f-space. I
am a bit concerned about whether the pseudoinverse might introduce
issues, but AFAICT in the practical examples I've tried, the
calculation seems to be above suspicion.
2. There remains the question of increasing the resolution in the
time domain (t-space). Well, if you have sampled at higher than the
Nyquist rate, and confirmed this by looking at the transform and
seeing no overlapped aliases, the sampling theorem teaches that you
can recover the time domain at any resolution desired, via the
interpolation formula involving the sinc function. No information
from the frequency domain (other than to confirm a lack of alias
overlap) is needed.
With respect to these 2 points, I do not see a way to perform a DFT,
increase the resolution in f-space, and then do an IDFT that achieves
a resolution in t-space that is higher than that given by the
original samples. But I would be interested in such an approach. We
are in fact inching toward the real question I want to ask, but I
want to first see what comments there might be about my above
speculations.