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Old 08-03-2014, 12:42 AM
ericwbenson (Eric)
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Cheers Eric, yes I was aware that decon worked better with greater sampling. Wonder if CCD sharp would work with a Starlightxoress camera...?

Mike
yep it works with FITS compatible files, as long as they are saved with integer data (as opposed to floating point which Maxim does after stacking, just make sure to stretch it back to 0-65535)

EB
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  #22  
Old 08-03-2014, 09:05 AM
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Firstly I just want to say "thank you" to Ray for the great write-up and discussion. I've never had access to astronomy software to do any form of deconvolution but I think you've convinced me that I should plonk down some cash for PixInsight.

Secondly, if it hasn't been made clear already, I'd like to say that iterative deconvolution is a process of separating the observed from a model of the noise in the image - in our case the atmosphere. Of course the atmosphere is ever-changing and we take many images over long time periods so the blurring effect that we see will vary slightly over the image frame, so _yes_ it will never be perfect. Even if it never changed we will also come up against losses of precision in our computers and this will cause some loss of accuracy.

By comparison, sharpening is a (generally) simple matrix math function which shifts affects contrasts based upon their surroundings in a way that we decide. There is often a considerable loss of information from this process (as information from higher orders is concentrated in lower orders). Personally I think the only place for sharpening is as a final, mild step to make an image "contrasty" for its final medium - on screen or in print.

Cheers,
Cam
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Old 08-03-2014, 10:38 AM
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Firstly I just want to say "thank you" to Ray for the great write-up and discussion. I've never had access to astronomy software to do any form of deconvolution but I think you've convinced me that I should plonk down some cash for PixInsight.

Secondly, if it hasn't been made clear already, I'd like to say that iterative deconvolution is a process of separating the observed from a model of the noise in the image - in our case the atmosphere. Of course the atmosphere is ever-changing and we take many images over long time periods so the blurring effect that we see will vary slightly over the image frame, so _yes_ it will never be perfect. Even if it never changed we will also come up against losses of precision in our computers and this will cause some loss of accuracy.

By comparison, sharpening is a (generally) simple matrix math function which shifts affects contrasts based upon their surroundings in a way that we decide. There is often a considerable loss of information from this process (as information from higher orders is concentrated in lower orders). Personally I think the only place for sharpening is as a final, mild step to make an image "contrasty" for its final medium - on screen or in print.

Cheers,
Cam
Thanks Cam.
In addition to your comments, I guess that the other distinction is that deconvolution only works properly on data that is still linear, whereas ad-hoc sharpening can be applied to stretched data. One is a formal restoration process, the other is cosmetic. I sometimes wonder if this might explain the occasional dodgy result from deconvolution - it has been applied to stretched data.
regards ray

Last edited by Shiraz; 08-03-2014 at 10:50 AM.
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Old 08-03-2014, 12:45 PM
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Thanks Cam.
In addition to your comments, I guess that the other distinction is that deconvolution only works properly on data that is still linear, whereas ad-hoc sharpening can be applied to stretched data. One is a formal restoration process, the other is cosmetic. I sometimes wonder if this might explain the occasional dodgy result from deconvolution - it has been applied to stretched data.
regards ray
Great discussion Ray,

further to your comment above,
I wonder if there is any merit in applying the deconvolution to the individual calibrated
subs rather than the raw stack result, pre stretch?

Steve
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Old 08-03-2014, 01:01 PM
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Good point Ray, thanks for mentioning that.

Steve - as long as no stretching is done during the stacking process then all that's going on is to average out the noise and signal in the original images so it should be okay.

Maybe instead of thinking of deconvolution as a sharpening process they should be thinking of it as a calibration process? That is really what it is provided the original signal is respected.
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Old 08-03-2014, 04:12 PM
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Great discussion Ray,

further to your comment above,
I wonder if there is any merit in applying the deconvolution to the individual calibrated
subs rather than the raw stack result, pre stretch?

Steve
probably agree with Cam Steve - will have to give it some thought. what do you think?

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Good point Ray, thanks for mentioning that.

Steve - as long as no stretching is done during the stacking process then all that's going on is to average out the noise and signal in the original images so it should be okay.

Maybe instead of thinking of deconvolution as a sharpening process they should be thinking of it as a calibration process? That is really what it is provided the original signal is respected.
makes a lot of sense Cam - it is clearly a calibration process in that it corrects the data for deficiencies in the imaging process.

regards ray
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Old 08-03-2014, 04:46 PM
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Thanks Cam.
In addition to your comments, I guess that the other distinction is that deconvolution only works properly on data that is still linear.
regards ray
I dont think that applies generally at all, why do you say that?. As Startools tracks stretching and noise during processing, it offers "mathematically correct"
(whatever that means) deconvolution before or after stretching. Ive done this many times and it seems to make no difference whether the image is linear or stretched. I havent directly A/B compared results BTW, thats just anecdotal fiddling around. I dont think CCD stack or Pixinsight can do this, is that what you mean?.

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Old 08-03-2014, 05:42 PM
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Hi Fred

In stretched data, the PSF varies with brightness (eg bright stars have different FWHM than dim ones). As far as I can see, deconvolution will not work properly if you cannot define a consistent PSF.

According to Ivo on the StarTools forum: As of version 1.3, the difference between linear (for which screen stretching is needed) and non-linear data has been abstracted away; all operations now *appear* non-linear, but some are, in fact, performed on linear versions of the data in the background. StarTools accomplishes that by 'going back in time' while your data was still linear, applying the operation to that version of the data, then calculating how the result would have looked after all the steps you performed after you decided to apply the linear step; it's akin to time travel where you change the past in order to change the future.

What this means for you is that you don't have to worry about keeping your data linear for, for example, deconvolution - you can now apply mathematically correct deconvolution any time, for example after stretching, HDR optimisation, Wavelet Sharpening, etc.) - it will still work! StarTools will calculate the result for you as if you performed deconvolution before you applied stretching, HDR optimisation, Wavelet Sharpening, etc. Neat hey? It's ultimate freedom! And not to mention much more user friendly...


I guess that what this means is that StarTools does not actually do deconvolution on stretched data. It will not allow deconvolution on data that is imported in a stretched form. It protects you from making a mess of your data by cleverly referring back to the linear data if you wish to apply deconvolution after stretching linear data in StarTools. Other packages are not so protective or flexible and, as a general rule, deconvolution should not be applied to stretched data - it won't work properly.

Last edited by Shiraz; 08-03-2014 at 07:12 PM.
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Old 08-03-2014, 06:37 PM
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Ok, thanks for that Ray (I should RTFM) . Makes total sense.
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Old 08-03-2014, 07:57 PM
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Ok, thanks for that Ray (I should RTFM) . Makes total sense.
..was good idea to bring up StarTools - it's another good package that offers deconvolution.
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Old 10-03-2014, 01:19 PM
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Hi,
To clarify, deconvolution cannot be considered a calibration step. Calibration is the removal of known and quantifiable system artefacts. Decon of atmospheric effects is never known apriori nor easily quantifiable. The problem of decon is an inverse problem with no single nor sometimes obvious solution.

Also decon should not be applied to single subs (unless you have only one sub!). As I mentioned in a previous post above, decon is basically a trade off of high SNR (because you have more than you need and can always collect more signal with more time) to spatial resolution which you cannot easily get more of even if you collect for ever at that location.

EB
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Old 10-03-2014, 02:18 PM
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Hi,
To clarify, deconvolution cannot be considered a calibration step. Calibration is the removal of known and quantifiable system artefacts. Decon of atmospheric effects is never known apriori nor easily quantifiable. The problem of decon is an inverse problem with no single nor sometimes obvious solution.
Two points here:
1) Noise may be quantified but only by estimating a distribution. You cannot know the exact noise term in each pixel and remove it perfectly leaving only a perfect image.

2) As I already mentioned in an earlier post, you cannot model right across the whole changing wavefront of the sky, so some approximation is always to be expected. This is no different from estimating the dark noise, bias noise, amplification noise or shot noise for any given pixel.

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Also decon should not be applied to single subs (unless you have only one sub!). As I mentioned in a previous post above, decon is basically a trade off of high SNR (because you have more than you need and can always collect more signal with more time) to spatial resolution which you cannot easily get more of even if you collect for ever at that location.
I've never seen this written before and I think it's not strictly true. Wikipedia states the following:
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Originally Posted by http://en.wikipedia.org/wiki/Deconvolution
In this case ε is noise that has entered our recorded signal. If we assume that a noisy signal or image is noiseless when we try to make a statistical estimate of g, our estimate will be incorrect. In turn, our estimate of ƒ will also be incorrect. The lower the signal-to-noise ratio, the worse our estimate of the deconvolved signal will be. That is the reason why inverse filtering the signal is usually not a good solution. However, if we have at least some knowledge of the type of noise in the data (for example, white noise), we may be able to improve the estimate of ƒ through techniques such as Wiener deconvolution.
This says that deconvolution will always be limited by SNR but it is NOT a trade-off. You imply with your statement that "you can always improve SNR" that the possibilities of decon are endless and that you could somehow achieve better resolution than half the wavelength of the light you are looking at - you can't. You can restore to this point, and the question of what can be resolved varies by empirical measure (Dawes, Rayleigh, etc), but you can't beat it, ever.

NB. There are tricks to doing so in microscopy - so called Super Resolution Microscopy - but these are actually methods of isolating what are effectively point sources and using these to reconstruct a whole image, they are not part of this discussion.
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Old 10-03-2014, 05:45 PM
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Hi,
Decon of atmospheric effects is never known apriori nor easily quantifiable.

EB
Hi Eric.
Agree that the atmosphere cannot be known a-priori, but I don't think that you need this - surely you can just measure the stellar profile in the stacked image at processing time and use that as your PSF for deconvolution. regards Ray
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Old 10-03-2014, 06:23 PM
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Two points here:
1) Noise may be quantified but only by estimating a distribution. You cannot know the exact noise term in each pixel and remove it perfectly leaving only a perfect image.
This is true, and hence why decon is a "wicked" problem.

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2) As I already mentioned in an earlier post, you cannot model right across the whole changing wavefront of the sky, so some approximation is always to be expected. This is no different from estimating the dark noise, bias noise, amplification noise or shot noise for any given pixel.
Not exactly sure what you're getting at. But remember calibration does not remove noise (dark, bias or shot, it actually adds some itself), it only removes unwanted signal (dark, bias, fixed pattern and vignetting), that is artificially added by the measurement system. Decon is not removing a measurement artefact, the smeared wavefront is the same no matter what telescope/camera is looking at it.
As a byproduct of decon optical aberrations can be 'removed' (if your algorithm is good enough, e.g. TinyTim for the blurry Hubble) since the decon assumes the original PSF was a symmetric gaussian/moffat etc, and that's where it steers the solution. I've never had any success with that however, bad data due to miscollimation or the like always sticks around to annoy...

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I've never seen this written before and I think it's not strictly true.
Well, here's another way to look at it, notwithstanding wikipedia : the image contains information, decon reallocates that information. Decon is not a free lunch, the amount of spatial resolution gained is limited by (among other things) how much signal to noise contrast you can/want to give up.

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This says that deconvolution will always be limited by SNR but it is NOT a trade-off.
Sorry I don't understand how you draw this conclusion.

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You imply with your statement that "you can always improve SNR" that the possibilities of decon are endless and that you could somehow achieve better resolution than half the wavelength of the light you are looking at - you can't. You can restore to this point, and the question of what can be resolved varies by empirical measure (Dawes, Rayleigh, etc), but you can't beat it, ever.
Wait a sec...you can always improve the SNR of the raw data, just collect more data (granted it's asymptotic but not if you get a bigger scope...)
Where did I say the possibilities of decon are endless?? They are in fact quite the opposite, decon in most cases gives a marginal or no real improvement to the image since it can easily create structures that aren't real by amplifying noise. Doing decon right is not easy at all, the best way is to have really good data to start with, and be very careful as you wield that sword.

EB
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Old 10-03-2014, 06:34 PM
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Hi Eric.
Agree that the atmosphere cannot be known a-priori, but I don't think that you need this - surely you can just measure the stellar profile in the stacked image at processing time and use that as your PSF for deconvolution. regards Ray
If you measure it from your image and it changes for every image and you can't remove it with a reversible mathematical transformation, I can't see how it can be called calibration.
Those semantics aside, yes the PSF of each star is the clue that allows deconvolution to enhanced the not point-source bits. It presumes (very important clause here, because it's not always right!) that the math that transforms the crappy PSF into the desired perfectly shaped/narrower gaussian also applies to your nebula etc.

EB
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Old 10-03-2014, 06:51 PM
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It presumes (very important clause here, because it's not always right!) that the math that transforms the crappy PSF into the desired perfectly shaped/narrower gaussian also applies to your nebula etc.

EB
Thanks Eric. That's a key assumption of deconvolution that Mike alluded to earlier.
Under what circumstances will deconvolution apply to stars but not to nebulae etc in the same image? - apart from the obvious and manageable issue of non-linearity/saturation.

EDIT: I guess the basic question is, which algorithms employ constraints that work against extended structures. My understanding is that some of the radio astronomy algorithms do so, but that the algorithms widely used in optical system are equally as capable of effective deconvolution on nebulae/galaxies as on stars. The widely used Ricardson-Lucy and van Cittert for example are fine for extended objects and will work properly using measured star profiles directly as PSFs for deconvolution on nebulae and galaxies.

Last edited by Shiraz; 10-03-2014 at 08:48 PM.
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Old 10-03-2014, 08:39 PM
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People get this strange idea that because perfect Airy-discs are used to model an ideal PSF for a star means that nebula images will somehow get screwed up by deconvolution. They won't - provided there are at least a couple of stars in there to train the blind deconvolution algorithms. The correction model that effectively "sharpens up" the stars will also be applied to the nebula, bringing it back closer to what it would be without distorted air and optics in the way.
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