<p>Drizzling is just one technique in the field of <A HREF="https://en.wikipedia.org/wiki/Super-resolution_imaging" rel="nofollow noreferrer">super resolution imaging</A>. Note that claims that you can't "pass the resolution limit" of a camera are wrong, in principle. In practice, it's really hard. What limits your ability to construct an image of what the camera is looking at are: the amount of random noise in the pixels, and your understanding of the camera's <A HREF="https://en.wikipedia.org/wiki/Point_spread_function" rel="nofollow noreferrer">point spread function (PSF)</A> (though some astronomers prefer to use the term "point response function" [PRF] if you include the effects of both the optics and the detector). In principle, if you invest enough time and money to nail down the PSF and increase the signal to noise ratio without limit (beware of bias), the object you're imaging isn't changing, and you have enough accuracy in measuring how you point the camera when dithering, then, in principle, you can push the resolution of the reconstructed image as far as you want.</p>

<p>In practice, it's usually cheaper and more time efficient to just get a bigger camera.</p>

<p>For a concrete mathematical example of how image reconstruction works, consider the sum of two offset <A HREF="https://en.wikipedia.org/wiki/Normal_distribution" rel="nofollow noreferrer">Gaussians</A>, with $\sigma=1$, $\mu_1=-0.1$, and $\mu_2=+0.1$. Here's a graph of such a curve overplotted with a single Gaussian with the same $\sigma$, scaled to have the same height at $x=0$. You can only see one line plotted, though I assure you there are two. 
<a href="https://i.sstatic.net/IeHvf.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/IeHvf.jpg" alt="Plot of sum of Gaussians vs. single close Gaussian"></a></p>

<p>To see the difference between them, we have to plot the difference (plotting the residuals, in data analysis parlance). This next graph is the difference, zoomed in by a factor of 100 in the $y$-direction.
<a href="https://i.sstatic.net/ujKLE.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/ujKLE.jpg" alt="enter image description here"></a></p>

<p>As you can see, the theoretical difference is not zero. So, if what you're imaging isn't changing, you can model the background accurately enough, and you have a sufficiently accurate model of the camera's PSF, you could detect the difference between the high Gaussian and two slightly offset Gaussians scenarios if you invest enough time and energy into the project, in principle, even if your pixels are twice the full-width at half maximum (FWHM) of the Gaussian. $\mathrm{FWHM} = \sqrt{8\ln(2)}\sigma$ for Gaussians, so in this graph twice the FWHM is about $4.7$.</p>

<p>One way to further the goal of understanding the PSF is to intentionally make it easy to model (as opposed to 'as small as possible'), as is done in many forms of <A HREF="https://en.wikipedia.org/wiki/Aperture_masking_interferometry" rel="nofollow noreferrer">non-redundant aperture mask imaging</A>. I believe that the trade-off there is that you lose both field of view and the (obvious) loss of the light that gets blocked.</p>

<p>In practice, though, you're usually better off getting a bigger camera, or compensating for the atmosphere (e.g. <A HREF="https://en.wikipedia.org/wiki/Lucky_imaging" rel="nofollow noreferrer">lucky imaging</A> and <A HREF="https://en.wikipedia.org/wiki/Adaptive_optics" rel="nofollow noreferrer">adaptive optics</A>) if your imaging is seeing limited. Thus, the limits imposed by the <A HREF="https://en.wikipedia.org/wiki/Nyquist%E2%80%93Shannon_sampling_theorem" rel="nofollow noreferrer">Nyquist sampling theorem</A> are more practical, not theoretical. It's also prone to mislead you if you don't remember that pixels are not points, they're little buckets that collect light that falls into a finite area. Talking about it "sampling" some function, implicitly uses the <A HREF="https://en.wikipedia.org/wiki/Mean_value_theorem" rel="nofollow noreferrer">mean value theorem</A> to say that the amount of light collected divided by the pixel's area gives the value of the image's brightness there. What's often dropped is the caveat that it's the brightness <em>somewhere inside the pixel</em>, not necessarily at the pixel's center. This is usually a very small effect, which is why it's often ignored, but when you're talking about working at the resolution limit and having pixel sizes comparable to that resolution, the distinction may become important. Roughly speaking, if the PSF is highly <A HREF="https://en.wikipedia.org/wiki/Curvature" rel="nofollow noreferrer">curved</A> within the pixel, the offset from pixel center of the mean value can become significant.</p>

<p>One way to think about what <A HREF="https://en.wikipedia.org/wiki/Drizzle_%28image_processing%29" rel="nofollow noreferrer">drizzle</A> gets you is that by moving the pixels between images, you move around the light buckets. In principle, with enough images at different offsets you can probably push the pixel density of the image to the limit of your ability to reconstruct the size of the offsets (usually the pointing of the camera/telescope, though I know of no reason why the sensor couldn't be moved around on some stepper motors attached to micrometers), or the sharpness of the divides between the pixels (the ability to gather light is neither uniform over a pixel, nor could the pixels fill the detector from edge to edge completely, these are just really good approximations most of the time).</p>

CC BY-SA 3.0
2017-10-18T20:24:45.823Z
Isn't the PSF usually measured by the stars in the image? It shows how well defined star peaks are, how eccentric, etc. That PSF would obviously already include the optical factors coming from the telescope and any errors in the guiding during the long time exposure.

Usually that PSF of the stars is estimated in PixInsight using the DynamicPSF process and then used in Deconvolution process so that it can converge the stars on your image to get smaller / closer to your optimal PSF
RononDex
CC BY-SA 3.0
2017-10-18T20:26:33.623Z
Also thanks for showing these functions, I had some data analysis during my part time studies in physics in Zurich, so that helps me in understanding it :)
RononDex
CC BY-SA 3.0
2017-10-18T20:27:37.287Z
Also I usually drizzle my images during capture, meaning when one 600s exposure is done, it tells the guiding software to move by xx pixels and then start the next 600s, and since thats usually a floating number like 2.5pixels or so you distribute the noise pretty well over the whole image, making it easier for drizzle integration to work later on
RononDex
CC BY-SA 3.0
2017-10-18T20:29:28.997Z
Yes, the PSF is usually measured using stars of varying brightness, and it can change in shape across the image. Worse, the PSF is technically bigger than the image (in radio astronomy they go all the way to [side lobes](https://en.wikipedia.org/wiki/Side_lobe)), so you can have problems with sufficiently bright objects outside of the image.
Sean Lake
CC BY-SA 3.0
2017-10-18T20:41:09.777Z
that's interesting, didnt know that about radio astronomy! 
Yes what I usually do is select around 100 stars all around the image to take coma, flat field etc into account and then average them
RononDex
CC BY-SA 3.0
2017-10-18T20:44:21.413Z
@RononDex "Also I usually drizzle my images during capture…" Drizzle is the name of a particular algorithm for combining multiple images. The general technique that Drizzle is an example of is called "co-addition". The WISE project, for instance, used [ICORE](http://irsa.ipac.caltech.edu/applications/ICORE/docs/icore.pdf). What you're describing here isn't drizzling, it's dithering. It's more widespread in IR imaging to work around detector flaws. See, eg https://www.noao.edu/kpno/manuals/flmn/FLMN-4m.pdf
Sean Lake
CC BY-SA 3.0
2017-10-18T20:46:47.950Z
ah yeah I mixed up the terms there, thanks for the explanations :) I am still bachelor student and learning :)
RononDex
CC BY-SA 3.0
2017-10-18T20:47:23.983Z
No problem. Glad I could help, and I hope you keep your enthusiasm up! :)
Sean Lake
CC BY-SA 3.0
2017-10-20T03:56:18.253Z
This is a really nice answer! One comment, "...you can push the resolution of the reconstructed image as far as you want." might be a little misleading to some readers. Deconvolution has problems when applied to realistic data.
uhoh
CC BY-SA 3.0
2017-10-20T17:02:52.047Z
@uhoh I think with the new edit that I've added enough caveats to show the difficulties in actually pushing past the camera's native resolution. Also, Deconvolution is just one of the super-resolution techniques available.
Sean Lake
CC BY-SA 3.0
2017-10-20T17:28:11.927Z
That's perfect - "in principle" are the magic words. I think this is a really great answer; one of those that can be linked back to as reference in the future.
uhoh
CC BY-SA 3.0
2017-10-18T19:36:27.933Z
2017-10-20T17:00:40.927Z
2017-10-20T17:00:40.927Z
Sean Lake
<p>Drizzling can't actually do any better than the theoretical resolving power of the combination of your telescope and the atmosphere. What it <em>can</em> do is at least partly compensate for having pixels that are too large to properly sample the resolution of your telescope + the atmosphere.</p>

<p>The <a href="https://en.wikipedia.org/wiki/Nyquist%E2%80%93Shannon_sampling_theorem" rel="noreferrer">Nyquist Sampling Theorem</a> basically says that you should have at least 2 pixels per resolution element in order to properly sample the resolution. Or, conversely, if you want to achieve a resolution of X arcsec, you want pixels that are no more than X/2 arcsec in size. So if the theoretical combination of your telescope diameter and the atmosphere had a resolution of 0.5 arcsec (so we're talking about really good atmospheric conditions), you want pixels that are 0.25 arcsec or smaller; otherwise, the pixelization overwhelms the intrinsic resolution. But note that you don't really gain anything from going to even smaller pixels: you'll just have a nice blurry image without visible pixelization. Having pixels that are 0.1 arcsec in size won't make the image any sharper than pixels that are 0.2 arcsec in size, if the resolution of your system (telescope + atmosphere) is ~ 0.5 arcsec.</p>

<p>In your hypothetical case, 0.52-arcsec pixels are good enough for resolutions down to about 1 arcsec. Ideal drizzling would allow you to effectively have pixels that were about 0.25 arcsec in size, which would accommodate a resolution of about 0.5 arcsec. But you can never do <em>better</em> than the intrinsic resolution of your telescope + the atmosphere.</p>

CC BY-SA 3.0
2017-10-18T10:55:33.143Z
I see, that's an interesting read, thanks for the answer :)
RononDex
CC BY-SA 3.0
2017-10-18T11:01:23.153Z
What I don't really understand though, it the part where you say that 0.25arcsec resolution would allow for 0.5 arcsec resolution on the picture or the Nyquist sampling theorem you linked.
RononDex
CC BY-SA 3.0
2017-10-18T13:40:42.993Z
I'll take minor exception to your statements about atmospheric limit, since a good A-O  (adaptive optic) compensation system can remove a lot of the potential degradation.
Carl Witthoft
CC BY-SA 3.0
2017-10-18T13:59:39.930Z
Well, the original question was talking about an 8-inch telescope, so worrying about AO is complicating things a bit in that context...
Peter Erwin
CC BY-SA 3.0
2017-10-18T14:04:15.893Z
RononDex -- the idea is that if you want to be able to distinguish details as small as X (= have a "resolution" of X), you need to *sample* the data at half that size (e.g., have pixels that are X/2 or smaller in size). Otherwise, you'll have an image that's dominated by the blockiness of your pixels.
Peter Erwin
CC BY-SA 3.0
2017-10-17T18:56:52.220Z
2017-10-18T06:10:00.507Z
2017-10-18T06:10:00.507Z
Peter Erwin
<p>So I was wondering, if <a href="https://en.wikipedia.org/wiki/Drizzle_(image_processing)" rel="noreferrer">drizzle integration</a> actually increases details in your image if the drizzled image results in higher resolution in terms of arcseconds / pixel than the telescope would allow (due to the uncertainty principle the telescope's resolving capability is dependent on its diameter, more commonly referred to as the diffraction limit <a href="https://en.wikipedia.org/wiki/Diffraction-limited_system#Implications_for_digital_photography" rel="noreferrer">1</a>, <a href="https://en.wikipedia.org/wiki/Airy_disk#Cameras" rel="noreferrer">2</a> )</p>

<p>In my case I have around 0.52" per px on my main camera and my GSO RC8" allows for around 0.5" resolution max due to the uncertainty principle. Drizzling an image would then give me 0.26" per px on the drizzled image when increasing width and height of the image by factor 2, which is higher resolving power than what the telescope would actually be able to do.</p>

<p>Apart from sharper and more defined stars when drizzle integrating, will drizzle integration actually increase my resolving capability?</p>

<photography><angular-resolution><rc-telescope><drizzle><post-processing>
Can Drizzle Integration increase resolution beyond theoretical resolution of telescope?
RononDex
# Can Drizzle Integration increase resolution beyond theoretical resolution of telescope?

(Asked by: username_0 on 2017-10-17)

So I was wondering, if [drizzle integration](https://en.wikipedia.org/wiki/Drizzle_(image_processing)) actually increases details in your image if the drizzled image results in higher resolution in terms of arcseconds / pixel than the telescope would allow (due to the uncertainty principle the telescope's resolving capability is dependent on its diameter, more commonly referred to as the diffraction limit [1](https://en.wikipedia.org/wiki/Diffraction-limited_system#Implications_for_digital_photography), [2](https://en.wikipedia.org/wiki/Airy_disk#Cameras) )

In my case I have around 0.52" per px on my main camera and my GSO RC8" allows for around 0.5" resolution max due to the uncertainty principle. Drizzling an image would then give me 0.26" per px on the drizzled image when increasing width and height of the image by factor 2, which is higher resolving power than what the telescope would actually be able to do.

Apart from sharper and more defined stars when drizzle integrating, will drizzle integration actually increase my resolving capability?

**Tags:** photography, angular-resolution, rc-telescope, drizzle, post-processing


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## 2 answers

### Answer by username_3 on 2017-10-17. Score: 9 | Accepted answer

Drizzling can't actually do any better than the theoretical resolving power of the combination of your telescope and the atmosphere. What it *can* do is at least partly compensate for having pixels that are too large to properly sample the resolution of your telescope + the atmosphere.

The [Nyquist Sampling Theorem](https://en.wikipedia.org/wiki/Nyquist%E2%80%93Shannon_sampling_theorem) basically says that you should have at least 2 pixels per resolution element in order to properly sample the resolution. Or, conversely, if you want to achieve a resolution of X arcsec, you want pixels that are no more than X/2 arcsec in size. So if the theoretical combination of your telescope diameter and the atmosphere had a resolution of 0.5 arcsec (so we're talking about really good atmospheric conditions), you want pixels that are 0.25 arcsec or smaller; otherwise, the pixelization overwhelms the intrinsic resolution. But note that you don't really gain anything from going to even smaller pixels: you'll just have a nice blurry image without visible pixelization. Having pixels that are 0.1 arcsec in size won't make the image any sharper than pixels that are 0.2 arcsec in size, if the resolution of your system (telescope + atmosphere) is ~ 0.5 arcsec.

In your hypothetical case, 0.52-arcsec pixels are good enough for resolutions down to about 1 arcsec. Ideal drizzling would allow you to effectively have pixels that were about 0.25 arcsec in size, which would accommodate a resolution of about 0.5 arcsec. But you can never do *better* than the intrinsic resolution of your telescope + the atmosphere.

> **Comments:**
> **username_0**: I see, that's an interesting read, thanks for the answer :)  
> **username_0**: What I don't really understand though, it the part where you say that 0.25arcsec resolution would allow for 0.5 arcsec resolution on the picture or the Nyquist sampling theorem you linked.  
> **username_4**: I'll take minor exception to your statements about atmospheric limit, since a good A-O  (adaptive optic) compensation system can remove a lot of the potential degradation.  
> **username_3**: Well, the original question was talking about an 8-inch telescope, so worrying about AO is complicating things a bit in that context...  
> **username_3**: username_0 -- the idea is that if you want to be able to distinguish details as small as X (= have a "resolution" of X), you need to *sample* the data at half that size (e.g., have pixels that are X/2 or smaller in size). Otherwise, you'll have an image that's dominated by the blockiness of your pixels.  

---

### Answer by username_1 on 2017-10-18. Score: 6

Drizzling is just one technique in the field of [super resolution imaging](https://en.wikipedia.org/wiki/Super-resolution_imaging). Note that claims that you can't "pass the resolution limit" of a camera are wrong, in principle. In practice, it's really hard. What limits your ability to construct an image of what the camera is looking at are: the amount of random noise in the pixels, and your understanding of the camera's [point spread function (PSF)](https://en.wikipedia.org/wiki/Point_spread_function) (though some astronomers prefer to use the term "point response function" [PRF] if you include the effects of both the optics and the detector). In principle, if you invest enough time and money to nail down the PSF and increase the signal to noise ratio without limit (beware of bias), the object you're imaging isn't changing, and you have enough accuracy in measuring how you point the camera when dithering, then, in principle, you can push the resolution of the reconstructed image as far as you want.

In practice, it's usually cheaper and more time efficient to just get a bigger camera.

For a concrete mathematical example of how image reconstruction works, consider the sum of two offset [Gaussians](https://en.wikipedia.org/wiki/Normal_distribution), with $\sigma=1$, $\mu\_1=-0.1$, and $\mu\_2=+0.1$. Here's a graph of such a curve overplotted with a single Gaussian with the same $\sigma$, scaled to have the same height at $x=0$. You can only see one line plotted, though I assure you there are two.
[![Plot of sum of Gaussians vs. single close Gaussian](https://i.sstatic.net/IeHvf.jpg)](https://i.sstatic.net/IeHvf.jpg)

To see the difference between them, we have to plot the difference (plotting the residuals, in data analysis parlance). This next graph is the difference, zoomed in by a factor of 100 in the $y$-direction.
[![enter image description here](https://i.sstatic.net/ujKLE.jpg)](https://i.sstatic.net/ujKLE.jpg)

As you can see, the theoretical difference is not zero. So, if what you're imaging isn't changing, you can model the background accurately enough, and you have a sufficiently accurate model of the camera's PSF, you could detect the difference between the high Gaussian and two slightly offset Gaussians scenarios if you invest enough time and energy into the project, in principle, even if your pixels are twice the full-width at half maximum (FWHM) of the Gaussian. $\mathrm{FWHM} = \sqrt{8\ln(2)}\sigma$ for Gaussians, so in this graph twice the FWHM is about $4.7$.

One way to further the goal of understanding the PSF is to intentionally make it easy to model (as opposed to 'as small as possible'), as is done in many forms of [non-redundant aperture mask imaging](https://en.wikipedia.org/wiki/Aperture_masking_interferometry). I believe that the trade-off there is that you lose both field of view and the (obvious) loss of the light that gets blocked.

In practice, though, you're usually better off getting a bigger camera, or compensating for the atmosphere (e.g. [lucky imaging](https://en.wikipedia.org/wiki/Lucky_imaging) and [adaptive optics](https://en.wikipedia.org/wiki/Adaptive_optics)) if your imaging is seeing limited. Thus, the limits imposed by the [Nyquist sampling theorem](https://en.wikipedia.org/wiki/Nyquist%E2%80%93Shannon_sampling_theorem) are more practical, not theoretical. It's also prone to mislead you if you don't remember that pixels are not points, they're little buckets that collect light that falls into a finite area. Talking about it "sampling" some function, implicitly uses the [mean value theorem](https://en.wikipedia.org/wiki/Mean_value_theorem) to say that the amount of light collected divided by the pixel's area gives the value of the image's brightness there. What's often dropped is the caveat that it's the brightness *somewhere inside the pixel*, not necessarily at the pixel's center. This is usually a very small effect, which is why it's often ignored, but when you're talking about working at the resolution limit and having pixel sizes comparable to that resolution, the distinction may become important. Roughly speaking, if the PSF is highly [curved](https://en.wikipedia.org/wiki/Curvature) within the pixel, the offset from pixel center of the mean value can become significant.

One way to think about what [drizzle](https://en.wikipedia.org/wiki/Drizzle_%28image_processing%29) gets you is that by moving the pixels between images, you move around the light buckets. In principle, with enough images at different offsets you can probably push the pixel density of the image to the limit of your ability to reconstruct the size of the offsets (usually the pointing of the camera/telescope, though I know of no reason why the sensor couldn't be moved around on some stepper motors attached to micrometers), or the sharpness of the divides between the pixels (the ability to gather light is neither uniform over a pixel, nor could the pixels fill the detector from edge to edge completely, these are just really good approximations most of the time).

> **Comments:**
> **username_0**: Isn't the PSF usually measured by the stars in the image? It shows how well defined star peaks are, how eccentric, etc. That PSF would obviously already include the optical factors coming from the telescope and any errors in the guiding during the long time exposure.  Usually that PSF of the stars is estimated in PixInsight using the DynamicPSF process and then used in Deconvolution process so that it can converge the stars on your image to get smaller / closer to your optimal PSF  
> **username_0**: Also thanks for showing these functions, I had some data analysis during my part time studies in physics in Zurich, so that helps me in understanding it :)  
> **username_0**: Also I usually drizzle my images during capture, meaning when one 600s exposure is done, it tells the guiding software to move by xx pixels and then start the next 600s, and since thats usually a floating number like 2.5pixels or so you distribute the noise pretty well over the whole image, making it easier for drizzle integration to work later on  
> **username_1**: Yes, the PSF is usually measured using stars of varying brightness, and it can change in shape across the image. Worse, the PSF is technically bigger than the image (in radio astronomy they go all the way to [side lobes](https://en.wikipedia.org/wiki/Side_lobe)), so you can have problems with sufficiently bright objects outside of the image.  
> **username_0**: that's interesting, didnt know that about radio astronomy!  Yes what I usually do is select around 100 stars all around the image to take coma, flat field etc into account and then average them  
> **username_1**: @username_0 "Also I usually drizzle my images during capture…" Drizzle is the name of a particular algorithm for combining multiple images. The general technique that Drizzle is an example of is called "co-addition". The WISE project, for instance, used [ICORE](http://irsa.ipac.caltech.edu/applications/ICORE/docs/icore.pdf). What you're describing here isn't drizzling, it's dithering. It's more widespread in IR imaging to work around detector flaws. See, eg https://www.noao.edu/kpno/manuals/flmn/FLMN-4m.pdf  
> **username_0**: ah yeah I mixed up the terms there, thanks for the explanations :) I am still bachelor student and learning :)  
> **username_1**: No problem. Glad I could help, and I hope you keep your enthusiasm up! :)  
> **username_2**: This is a really nice answer! One comment, "...you can push the resolution of the reconstructed image as far as you want." might be a little misleading to some readers. Deconvolution has problems when applied to realistic data.  
> **username_1**: @username_2 I think with the new edit that I've added enough caveats to show the difficulties in actually pushing past the camera's native resolution. Also, Deconvolution is just one of the super-resolution techniques available.  
> **username_2**: That's perfect - "in principle" are the magic words. I think this is a really great answer; one of those that can be linked back to as reference in the future.  

---

