Hello- 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. -Jeff _______________________________________________ Gimp-user mailing list Gimp-user@lists.XCF.Berkeley.EDU https://lists.XCF.Berkeley.EDU/mailman/listinfo/gimp-user