This question isn't specifically related to Gimp, but I thought some
folks on this list would have the appropriate expertise to answer it.
So here goes...
I'm working on software that involves recognizing a certain pattern in
extremely blurry images. Thus far, standard deconvolution techniques
haven't adequately cleaned up the blur. So instead, I thought I might
try it the other way around -- that is, distort the pattern that I'm
looking for and then compare the distorted pattern to the actual image.
So basically, I have a series of specimen images taken with a
particular camera. For each one, I can precisely define what the
image is supposed to look like. So how would I empirically determine
the point-spread function or convolution kernel that would best
translate my model images into the specimen images?
Or am I asking the wrong question entirely?
Thanks in advance.
Gimp-user mailing list