vlaiv

Are you sure you have EFW the right way around? What EFW is it? ZWO as well? In fact it does not matter. EFW has both female T2 connections, your camera has T2 female connection, but EFW comes with T2-T2 adapter. This is how you should put together things: camera - t2-t2 - EFW - 16.5 extender - FF/FR It should all fit together like that, if I'm not mistaken.

Indeed, both my data as your data is not gathered with reducer, I plan to use binning as it has the same effect as reducer for this purpose - it provides the same aperture at resolution

I will do experiment with the data I have, but if you want, I can do it with the data you've gathered. No need to bin it, just post aligned and cropped (to remove any alignment / registration artifacts) set of subs and I'll do all the processing. Once I do experiment, you will be able to tell the difference between all versions - binned, native, reduced, enlarged ....

I think I get it now. Idea is to simulate reducer scenario? I can do following. Take data set, stack it at "normal resolution". Take half of that data set (half the subs) and bin it 2x2 to simulate reducer. Stack that one as well. Create comparison between images at large scale and small scale. Enlarge small image to match scale of larger image and do another comparison. I can do the same with quarter of the subs (that would be equivalent time when using x0.5 reducer or 2x2 binning - it should give same SNR as native resolution). Will do that and post results sometime today.

I don't really follow what you propose to be done? By half the data, do you mean half of the subs, or "every other pixel". I'm not getting the parts: "some fraction of the full size", and "we take it closer to full size" Once I understand your proposal, I'd be happy to give it a go and show the differences.

I think that answer has been given to that question, but to reiterate: In my words - yes. Olly's position on that again is clear from his last post on subject, and I'll quote: Here I'll point you to part: "you don't need the reducer". And in fact all of the rest said above is true and also demonstrated in this thread: - Bin x2 in practice will give you same results - Downsampling image will give you comparable results (and I made demonstration of how different resampling methods fare against binning). However, same as x2 bin works - so does reducer. In fact, in some cases reducer will have an edge over binning. For example if you are sampling at 1"/px and ideal sampling rate is 1.3"/px, and you happen to have reducer that reduces your focal length and gives you that sampling rate - it will be easier to use it than to bin - simply because you don't have suitable fractional binning method implemented in software yet. Reducer will provide better SNR improvement over other forms of resampling. There is another benefit of doing reducer vs binning - exposure time. With reducer you are getting stronger signal per pixel and it is easier to beat read noise - you need shorter subs. If sharpness of your image depends on your sub duration (shorter subs are sharper than longer subs due to guiding issues or perhaps wind or whatever) then reducer will perform better because you will be able to use shorter subs.

I think that you misinterpreted what Olly said there. What I'm reading (and happy to be corrected) is that Olly is saying in fact: Yes you can upsample it to same size, but like I pointed out - there is no useful detail to be had by doing so. That is not conflicting to what I said - no detail will be gained, in case of oversampling image will be the same because there was no detail in the first place, and I don't really see the point in doing so as image will be more pleasing at lower resolution and if people want to enlarge that image for better view - they can easily do it them selves by hitting magnify/zoom in button.

Well, yes if you know what you are doing I'm going to quote wiki article on Optical transfer function (https://en.wikipedi0.org/wiki/Optical_transfer_function) : Similarly you will get somewhat sharper image if you oversample and then do some tricks to restore lost SNR. This is one of the reasons for recent trend of having high megapixel sensors and decreasing pixel size. Coupled with low read noise of CMOS sensor that enables you to get sharper images then with larger pixels (other being that it just sells better when people hear it has "more" of something - more mega pixels ). In theory, ideal sensor will have extremely small pixels and 0 read noise. Such sensor would provide you with sharpest image and would offer fractional binning at any level without introducing artifacts. Due to nature of the world - it is simply not possible to make pixels smaller than wavelength of light (for example 2.4um pixel is about x4 larger than red wavelength so you can't get much smaller than that and still manage to record photons) and there will always be some read noise.

In principle you can do that and it will lead to faster imaging time (or better image in the same time). Same thing can be achieved by not using focal reducer and binning your data (almost the same thing - there are very minute differences related to pixel blur and read noise, but you will be hard pressed to see them). You can even do what you proposed at one point - if your native FL is oversampling, use focal reducer to record smaller image of object more quickly and then resample back to wanted size. However, I don't really see the point in resizing back to original size - no detail will be achieved that way (and that is ok because no detail was present in the first place when oversampling) and resulting image will look less sharp. You can leave it at lower resolution / smaller object - it will be aesthetically more pleasing to look at and if someone wants to look at it enlarged (to the same effect as if you upsampled it as part of processing) - they can just hit magnify buttom (ctrl + in their browser) - and image will be enlarged. Who ever chooses to do this will be aware that blur is consequence of enlargement and will not think that your image is at fault (but can object to this if you do it in processing). In practice there will be no difference assuming you bin x2 vs use x2 as large pixel (x4 in surface area - x2 width x2 height). In theory there are subtle differences, especially if you use software binning. There will be difference in read noise for binned camera - it will double "per pixel" - so instead comparing camera with smaller pixel base read noise to camera with larger pixels - compare double that value. For ASI1600 bin x2 will give 7.6um pixels, so when comparing to camera that has 7.6um pixels - use 3.4e read noise instead of 1.7e read noise. If you do binning certain way, for the same conditions you will have slightly sharper image - this is due to pixel blur. Surface of the pixel causes some blur over all other things that blur image (atmosphere, aperture, tracking/guiding). It is very small difference but it is there. Larger the surface of the pixel - larger the pixel blur. Regular binning will do the same to pixel blur as using larger pixel - so no advantage there. But other types of binning will circumvent issue with pixel blur to some degree. There are a few more differences that are worth mentioning in "academic" sense. Issue with hot pixels. When you software bin you effectively loose "1/4 of pixel value" due to hot pixel. With camera with larger pixels that is whole pixel. Similarly you can bin in a certain way to include the fact that not all pixels have the same read noise (some are more noisy than others) so you can do weighted average and improve read noise characteristics. I listed these subtle differences just that people get informed about them, but in practice you won't see almost any difference between camera with larger pixels and binning camera with smaller pixels.

Well, PI documentation does offer some insight in how it's all done - and what options are available: https://pixinsight.com/doc/tools/Resample/Resample.html https://pixinsight.com/doc/docs/InterpolationAlgorithms/InterpolationAlgorithms.html In fact in this last link you will see math behind it and effects it has on image and noise in some scenarios

Not sure if people will understand what "workable sampling rate" is. Imager might benefit from reducer if they are properly sampled at native focal length or even undersampled. In fact they will benefit from focal reducer in terms of speed if they leave sampling rate as is, and are happy with their sampling rate - image will reach target SNR faster. Some time ago, when I first started taking interest in whole F/ratio - speed stuff, I also tried to come up with meaningful measure of how fast system is. Something that can be used to compare two setups in terms of speed. It is possible to do, but defeats the point - too much math involved to be done quickly mentally (things need to be squared and divided). Only decent thing that I got out of it is "aperture at resolution" - but again that does not help much in this case and in general case because resolution changes. Even if we define some measure - it will be more of "setup is capable to deliver" type of thing but it will not be relative measure in all scenarios (like saying setup A is two times as fast as setup B). Whenever we have weak signal - other things start being more important than aperture and sampling rate. Processing workflow can have effects as well - like number of calibration frames and algorithms used.

You have couple of options there, not all are available in PI. You can do fractional binning. That is whole new topic, and I'm just mentioning it as information. Next thing to do would be simple resample. Depending on type of interpolation algorithm used for resampling you will get different results in SNR improvement (I'll expand on that with examples). Next thing that you could do is bin 2x2 to get to 2"/px and then upsample image back to 1.8"/px. This one will "cost" some of detail - but it's likely that most people won't be able to tell the difference. It will provide you with precise SNR improvement. Here is one setup that will show effects of different resampling methods. It will be "artificial" / generated example, but because of that we will be able to get exact numbers. I've created an image consisting out of two elements - a gaussian star profile with FWHM 2" and Gaussian noise with magnitude 1. Image is "sampled" at 0.5"/px. Here is image and measurements: This image can be reduced to half its size ("sampling" at 1"/px) without loss of information - It is oversampled. I'm going to use different methods for reducing it and we can compare impact on SNR (measurement of noise - signal stays the same when resampling so any reduction in noise will be improvement in SNR) and also on any resolution loss - by examining how FWHM changes in reduced image. In order, I will perform: 1. Nearest neighbor resampling (or rather just taking every other sample) - this approach should not have any impact on SNR and resolution what so ever 2. Regular binning 2x2 - it should reduce noise by factor of 2 and have small increase in FWHM due to pixel blur 3. Split / Sift binning - that is something I developed, so we can compare it to the others 4. Resampling with use of linear interpolation 5. Resampling with use of cubic interpolation 6. Cubic B-Spline 7. Cubic O-Moms 8. Quintic Spline Here is a screen shot of first option: As predicted, noise remains the same, and FWHM is also about the same (both vary because image is noisy) - now we are at 1"/px - so baseline FWHM is 2". Other measurements I'll just list instead of taking screen shots. First SNR: Results are in StdDev column. Baseline, and nearest neighbor have no change in SNR. Binning x2 and linear interpolation give SNR increase (predictably) by factor of x2. In this particular case linear interpolation is in fact bin x2 with half pixel shift (bin is average of two pixels, and because we are doing resizing by factor of x2 - linear interpolation is %50 of one pixel and 50% of other - which is the same as average). Split/Sift bin gives the best results. We can go a bit deeper into that, but I've already gave outline explanation for this in thread on software binning that I gave link to in one of earlier posts. Other advanced resampling methods give poorer results. That is to be expected because advanced resampling methods are designed to do least alteration to image when resampling - that includes noise as well as data. In fact - most advanced resampling here gives SNR increase of only ~x1.2 vs split/sift bin that results in ~2.22 boost in SNR. Let's now look at effects on resolution: FWHMx 2.0762 FWHMy 2.086 FWHMx 2.0164 FWHMy 2.0235 FWHMx 2.0762 FWHMy 2.086 FWHMx 2.0007 FWHMy 2.0125 FWHMx 1.9842 FWHMy 1.9988 FWHMx 1.9798 FWHMy 1.9957 FWHMx 1.9778 FWHMy 1.9946 Due to pixel blur, regular binning increases star FWHM by almost 4% - 2.08" vs 2". Same is of course true for bilinear resampling. Split/Sift bin increases FWHM by about 1% (it's designed to circumvent pixel blur). More advanced resampling methods pretty much keep FWHM the same - they are designed so that they don't loose any information. This little exercise also shows that for FWHM 2" stars, you don't loose anything when sampling at 1"/px - in fact, proper sampling for 2" FWHM image is about 1.25"/px (FWHM divided with about 1.6).

In principle I agree with that statement, however it is missing important piece of information to make it complete. What type of resampling are we talking about? There are different resampling algorithms with different properties. For example - nearest neighbor resampling will make 0 improvement in SNR - it will leave SNR exactly the same. Binning as a form of resampling has very predictable improvement in SNR. Other forms of resampling will have SNR improvement somewhere in between (or even more due to pixel to pixel correlation). Like I mentioned before, difference between binning and using equivalent focal reducer is in read noise contribution and covered FOV. Other forms of resampling (those that are worth using) will have slightly smaller SNR benefit compared to binning / equivalent reducer. On the other hand upsampling behaves differently. For most people that don't resample and have no clue about binning and such - focal reducer will provide real benefit in SNR, especially if they are oversampling at native focal length.

Yes, that is because Olly omitted effects of pixel scale in his argument, or rather per pixel SNR.

In fact you can There is a case where such action will result in virtually same image quality, and there is a case when such action will result in very small change in image sharpness. If you oversample when imaging at native focal length to such extent that using focal reducer provides proper sampling or oversampling (but not undersampling) for given conditions, you will get the same image in less time. As explained above - SNR per unit sky area does not change with use of focal reducer. What changes is mapping of that sky to pixels, and per pixel SNR does in fact change. Image recorded with focal reducer will have better SNR for same time, or will reach same SNR in less time than your oversampled image at native focal length. I've already shown in this thread that upsampling of the image does not reduce SNR. So image recorded with focal reducer will keep SNR when enlarged. Only thing that can happen in this process is that you potentially loose some of the information, and only in the case that image with focal reducer is undersampled. If that is not the case - you will get the same image.

Ah, yes, now I remember. You are in fact right saying that SNR per unit sky area can't be increased with focal reducer. No question about it - it is equivalent to my statement above that regardless of how much FOV is occupied by object - same aperture will gather same number of photons for that target. However, I do think that you should be careful when saying: as in your own words: For most people that don't contemplate what happens to such depths as we do in this and similar discussions, focal reducers provide real benefit. They won't be resampling / binning their data, and if data is not altered in such way post acquisition - it will indeed have higher SNR with focal reducer. Target SNR will be reached in shorter time and image will look less noisy / smoother when observed at 1:1 magnification. Target will of course be smaller due to coarser sampling rate. Ultimately, as seen from this example and earlier discussions, coming from you as well known and accomplished imager - such words have weight and people might get confused because they don't read "the fine print".

I see it the myth in couple of ways, and one is related to read noise. 1. Myth - faster F/ratio scope is faster to get to target SNR over slower scope 2. Myth - two scopes of same F/ratio are "equal in speed" (myth is myth even if we use same camera / pixel size). While first one incorporates read noise part - it is not in the hart of it. For second myth - read noise is crucial part (and so is dark current noise). Second myth (or part of the myth) can be reformulated and is often used that way - scope of let's say F/5 ratio is x4 time as fast as F/10 ratio (or however those F/stops work, never could remember that). This part is true only if there are noise sources dependent on aperture and no independent noise sources. Shot noise depends on aperture - because it is related to target signal, and LP noise depends on aperture - for the same reason, it is related to sky signal. Read noise depends on number of readouts and dark current noise on duration of exposure - not related to aperture / f/ratio and remain the same. Once you are in low light regime - both read noise and dark current noise become important contributors and can't be neglected anymore - they change above statement to: F/5 scope will be at most x4 faster than F/10 scope, but in reality it will depend on how bright target is and can be only faster by small percentage.

Yes, you are in fact right - I'm somehow always struggling with SNR (bortle 8 skies? ) and although I love max possible detail, sometimes better SNR just wins. If you already have plenty of SNR, then yes, simple resample to get stars tight enough is all that you need. Another interesting topic is mosaics vs single shot with smaller scope. It is related to this, and if I'm not mistaken here it goes - Same speed scopes used with same camera will result in "same" SNR regardless if you do mosaic or shoot single frame (they will be of the same speed). In practice this is not true because small things that you need to account for - like changing FOV (takes time), and slightly smaller FOV due to overlap needed to piece together mosaic. Read noise also plays a part but not important one - for CCDs it is the same, for CMOS it is higher, but longer subs deal with that. Here is simple "breakdown" of what happens. Let's observe simple case - same F/ratio, twice the focal length - consequently x4 collecting area. Bin x2 will result in same resolution. Large scope will collect x4 more light than small scope for same sampling rate (difference in read noise only) if we bin x2. You can only spend 1/4 of the time with large scope per panel in comparison to full FOV of small scope. In the end collected signal is the same and hence SNR (except for things mentioned before).

I already linked to a thread about software binning, so you can have a look on difference between resampling and binning in terms of SNR improvement. It is best done while data is still linear and probably best approach in your case would be to bin x2 to get from 1.24" up to 2.48" and then upsample back to 2"/px.

Here is interesting PDF by Craig Stark - it contains much of what we discussed here but there is even more - some very good examples of images and effects - it shows how one can have the same image by shooting reduced and then enlarging if image does not contain information in the first place. It also shows how much detail you loose by undersampling (depending on detail in the image) - I think it is very informative and sheds light on what is to be expected (and I'm guessing many will be surprised by subtle differences for example between 1"/px and 2"/px) - I also mentioned this above. http://www.stark-labs.com/craig/resources/Articles-&-Reviews/ImageSampling_Fratios_SNR_RTMC.pdf

I did hear (or better say read) once on Olly's position on this and yes as far as I remember he was not in agreement with this to an extent, but not sure it is still his position on the topic. But instead of hear/say - we can ask @ollypenrice what is his stand on all of this?

That is in fact true, and only thing that I would add to that is: - binning produces known improvement in SNR - binning is the form of resampling - depending on type of resampling you can get couple of things happen to your data - loss of resolution due to pixel blur and pixel to pixel correlation and improvement in SNR. - with regular binning you get pixel blur, but you can bin your data in such way that there is no increase in pixel blur. - most other resampling methods don't provide known increase in SNR, and often have poorer characteristics than binning (pixel to pixel correlation / improvement in SNR). - there is in fact fractional binning method that should provide benefits of integer binning and resampling. It introduces very small correlation, keeps pixel blur to a minimum and has predictable improvement in SNR (equal to "scale" of binning, or square root of binned surface). Only software that I've seen it implemented in is StarTools - and I'm guessing how it's done, and I believe there is a better way to do it ).

I would like to add to this that software binning is not always the best option - it is case dependent. If one is way oversampled at native FL - use of focal reducer might not result in optimal sampling rate. For example .78"/px with x0.7 reducer will result in 1.12"/px. If conditions (sky, scope, mount) don't allow for this resolution either - it is then better to bin as 1.56"/px will be closer to optimal sampling rate. If we go down that path we will change direction of this discussion quite a bit. I'm ok with that. First I would like us to define what is the "best" image. Then we can start discussing what is needed to create the best image, and after all of that we can see what combination of scope camera fits requirements for best image.

You are right - things that are not part of the galaxy will not contribute to light emitted from the galaxy - but read carefully what I've said. I was not talking about additional photons at all - they will be focused on a different place by telescope optics. They will come to aperture at a different angle and therefore will be at different place on the image. However - all the light from the galaxy will end up in the image of that galaxy - no more, no less. There we agree. Only thing that I was pointed out was spread of that light over pixels and consequent level of signal per each pixel - or SNR in the end.

That is absolutely correct - and is the origin of F/ratio myth. There is in fact one thing that is correct in statement that 1" scope at F/5 is equal to 100" scope at F/5 - and that is: if you use the same camera, don't care about resolution loss, then in principle they will be "equally" fast on signal that they can equally record. That is true in daytime / regular photography (or nearly true) - because it works in light dominated regime (plenty of light so no one cares about all the noise sources for the most part) and resolution is not severely impacted. It is also often used to characterize different lens on same camera - so pixel size does not change. In that world - it holds. It is only problematic when you try to extrapolate reasoning to cases when other factors start to dominate - like in astro imaging.