jwl@ernie.Berkeley.EDU (James Wilbur Lewis) (05/20/89)
In article <5300011@ux1.cso.uiuc.edu> phil@ux1.cso.uiuc.edu writes: > >Can this process of deblurring be applied where some points have a wider >spead than others, such as is the case with limited depth of field photos? The deconvolution algorithm I described assumes that the blurring process is constant over the field of view. The power at each point in frequency space depends on the whole image, including the blurred and unblurred parts of the image; similarly, the intensity of each spatial point depends on the frequency spectrum as a whole. So if you alter the frequency spectrum in an attempt to correct the defocused background, I expect you'll end up blurring the in-focus portions of the image. I think you might have to eyeball the image to break it up into contiguous regions where the point-spread function is constant, and apply the technique to each region. It sounds like a real hassle -- there's probably a better way to do it, but I don't know how. >Also, what will aperturing effects do to the process? This is when you >have a foreground subject exhibiting lots of apertures, and the background >having some peculiar shape, such as photographing a cresent moon through >a leafy tree, out of focus. The aperturing effect obvious distorts the >PSF of the background subject, but perhaps the background subject can help >define the aperturing pattern. Now this one might be solvable, assuming the moon is perfectly focused and you just want to get rid of the aperturing effects. The moon, at the image scale I think you're talking about, is a pretty high-contrast object -- basically a uniformly bright(*), very sharply defined object, meaning (I think!) little low-frequency information content. I'm guessing that the "aperturing" effects you're talking about are some sort of mottling of the moon's image by out-of-focus leaves. Since the leaves are defocused, their image will be missing the higher spatial frequencies. So you ought to be able to use a high-pass filter to seperate the low-frequency noise due to the aperturing from the high-frequency signal from the moon. As above, this will wreak havoc with the rest of the image, but you could crop out the part of the image containing the moon and just operate on that. I'd use an exponential roll-off instead of a "brick wall" filter to avoid ringing in the filtered image, and play around with different filter radii to see which one gives the best results. Geez. Couldn't you have just moved the camera so you wouldn't have to shoot through the trees? :-) If any of that sounds bogus, let me know -- this is strictly handwaving, and for all I know I could be bullshitting you blind... I'll x-post to sci.astro in case any of those folks want to take a stab at it. -- Jim Lewis U.C. Berkeley (*) yeah, i know about limb darkening, maria, and so on -- but a low-pass filter should get rid of all those unsightly blotches on your nice clean lunar image!
lupton@uhccux.uhcc.hawaii.edu (Robert Lupton) (05/21/89)
The problem of de-blurring images is pretty standard, and pretty hard. The naive solution (for a constant PSF) of deconvolving by dividing in the Fourier domain usually fails horribly. The problem is that the FT of the image usually dissapears into noise, and the noise is amplified. If you want to do better you have to use some constraints (such as the object is positive everywhere, or bounded by a circle). Various techniques are around, such as Jansson's (sp?) and Maximum Entropy. The rule of thumb in astronomy is that you can gain about a factor of 2 in resolution. Robert
james@rover.bsd.uchicago.edu (05/22/89)
In article <3985@uhccux.uhcc.hawaii.edu>, lupton@uhccux.uhcc.hawaii.edu (Robert Lupton) writes... > >The problem of de-blurring images is pretty standard, and pretty hard. The >naive solution (for a constant PSF) of deconvolving by dividing in the >Fourier domain usually fails horribly. Agreed. One other trick is a method called "Iterative Deconvolution". If you have an object that you can get on your image with approximately a known shape for your projection, you can make ane "estimate" of the actual PSF (point spread function), convolve it with the shape, and compare the result to the image. It is best to vary as few parameters as possible, and to assume a general shape for the PSF (eg. a gaussian). There are various articles in MEDICAL PHYSICS and RADIOLOGY on this technique as applied to the blurring function of radiographic imaging systems. James Balter james@rover.uchicago.edu "If the hat fits, slice it!"
cme@cloud9.Stratus.COM (Carl Ellison) (05/23/89)
"Digital image deblurring by nonlinear homomorphic filtering" Thomas Michael Cannon August 1974 UTEC-CSc-74-091 Computer Science Dept. University of Utah Salt Lake City, Utah 84112 In his example photos, there's considerable ringing around restored features in the moderately blurred shots (although all the blur is gone) and the severely blurred shot (of road signs) remains unreadable. --Carl Ellison UUCP:: cme@cloud9.Stratus.COM SNail:: Stratus Computer; 55 Fairbanks Blvd.; Marlborough MA 01752 Disclaimer:: (of course)