Super-resolution from highly overlapping photos seems hard. Maybe
impossible?
I have taken a series of night-sky images from a fixed camera over a period
of several hours. I identified keypoints (stars) in the images, and
correlated the keypoints across frames. This results in a file of ~1000
rows of 4 ints:
int FRAME_A, int STAR_B, int PIXEL_X, int PIXEL_Y (and timestamp of the
frame if it would be helpful)
The challenge is that it was taken with a wide-angle lens, and I don't know
the exact distortion parameters of the lens.
Goal: use the input file to either determine the lens model, or another
mathematical model (machine learning?), that will allow a very precise
function that maps a "true" night sky location to the coordinates in every
frame that covers that part of the sky. It is ok if the model takes a long
time to train, but the final lookup should be fast.
getAllOverlappingExactSourceLocations(x1, y1) ->
array of frames [{source_frame:frame_a, source_pixel_x:(float)x,
source_pixel_y:(float)y}, {source_frame:...]
This may be easier to think of in the inverse: given a panoramic landscape
shot taken with a slowly moving motorized camera, and choosing any ray from
the camera's location, get the intersections with that ray and all the
photos that overlap that ray.
It feels like this should be easy using Hugin or PTGUI: At the end of the
day, I've got very accurate keypoints, and I *should* be able to get an
accurate lens model from them... right? And once I have an accurate lens
model, I should be able to get each frame's yaw/pitch/roll with sub-pixel
accuracy. And then I... er... need help. How do I use this model + a
frame's yaw/pitch/roll to get source pixel locations for the final frame,
that is pretty much what the final stitcher is doing, true? So I'd need to
reimplement my own emblend? Any easier way?
--
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