michael.h.f.wilkinson

Actually, I have found APP quite happily merges old DSLR data with newer CMOS data, as I found out with M33 The slightly eggy stars are due to the field flattener not being at the right distance from the sensor, I believe

Lovely shot. Some of the tidal tail in NGC 3628 is showing as well, I think. I am hoping to add some cooled CMOS data from the ASI183MM-Pro to last years ASI183MC (uncooled) on this target one of these days

Magnificent shot!

Awesome stuff. I started on this pair only last year, and this really inspires me to add a lot more data this year

+1 for the mini-giro. It easily handles my dual solar set-up, either on a very light carbon-fibre tripod or on a home-made wooden tripod (with a tiny piece of EQ-2 tripod holding bits together). It can handle a lot more weight than just the 80mm. I am not a great user of go-to, especially for quick sessions. On outreach events I find I am up and running while others with go-to rigs are still trying to get star alignment working. By the time they can observe, I have spotted a couple of DSOs already (up to half a dozen). No go-to means no battery, means one less thing to go wrong or forget. Don't forget these wide-field refractors allow easy star-hopping, so a finder scope is hardly necessary. Regarding 4" vs 3": my 80mm is airline portable, I am not sure a 4" would be, but I am not sure it is a requirement.

It does look a bit odd. I will have a look at the paper to see whether I can understand why

So as I thought it is a wavelength rather than narrow-band effect

I did a comparison between my 80mm triplet and a pair of Helios Apollo 15x70s I used to have (since replaced by the Helios LightQuest 16x80). It might be useful:

Question: Why would stars be tighter in narrow-band imaging in a reflector like an RC? I have seen the effect in my APM triplet to some extent, but it is not noticeable in my Schmidt-Newton (and that might show sphero-chromaticity)

I am pretty sure I spotted these at a local camera store around 1980. They were also sold under the Revue brand at Foto-Quelle stores, as I recall

A flattener is typically designed for a specific field curvature (well, obviously), and this means a particular combination of focal length and objective design. Usually they work well over a certain range of focal lengths given an objective design (like doublet or triplet). If you are well away from that range, it will probably be sub-optimal, or at least should be used at a different distance from the CCD than the nominal value. The focal ratio is less important.

Very nice result indeed

Very nice image indeed. Still working on this target myself (I still need far more data without moonlight/haze)

Because there may be systematic drift in the system, like a rising moon, or you are combining two or more stacked images from two different sessions without resorting to restacking all the subs. In that case, either use the noise information in the FITS header (as computed by the stacking software in each session) or use the above-mentioned estimation method. For a derivation of the exponential distribution see this paper: http://www.cs.rug.nl/~michael/caip2003.pdf

As you may have seen I edited the response. You can estimate local variance quite easily using the distribution of the squared gradients in a region surrounding each pixel, discarding high gradients, and fitting an exponential distribution to the results. No iterations needed. In any flat area of the image, the squared gradients have an exponential distribution under the assumption of Gaussian noise (Poisson is close enough for large enough photon counts). This can be used if no calibration information is available. If we assume photon noise dominates (i.e. read noise is negligible), then this estimate is large superfluous, assuming we know the gain settings. Therefore, weighting by the inverse of the vairance is optimal in a least-squared sense.

PSNR is well defined. In practice, weighting by quality or estimated noise in the background works. More in general if you add signal weighted by the inverse of the variances you should get an optimal result, assuming independent noise.

I often combine data from different sessions in APP, and there are several options to weight different quality subs. I usually find weighting by quality or s/n works well.

As mentioned, I was using a Celestron C8, but since then I have used it in many more scopes, like the APM 80 mm F/6 triplet, my Meade SN6 6" F/5 Schmidt-Newton, and Olly Penrice's 20" F/4.1 Dobson. It always gives outstanding views. Neither the Schmidt-Newton nor the APM triplet require coma corrector, some coma was visible in the 20", but it wasn't very troublesome. It is still one of my favourite eyepieces.

Curious. Of course, chromatic correction is not an issue in a <1 nm bandpass, so expensive apochromatic scopes are overkill. Spherical aberration is usually more of an issue at the edge of the corrected range. A slow scope is bound to be better there. Having said that, I cannot say my APM 80mm F/6 hasn't disappointed in Ca-K, and seeing is almost always the limiting factor

I am fully up to speed with convolution (teach it in my Computer Vision class). If you blur any psf with a square the FWHM goes up, so conversely the spread of frequencies in the Fourier domain goes down. So the higher frequencies get drowned in noise earlier. This is just basic mathematics. The experience of most planetary imagers is that slight oversampling gets out more detail

Well, I will dig up his thesis one of these days to check his results

We have just had a paper published in IEEE Transactions on Image Processing, on a new method for processing images and volume data sets that the biggest (radio) telescopes like Meerkat and SKA are producing, or are going to produce. Many image processing tools lend themselves quite easily to parallel execution. However, the so-called connected morphological filters are very difficult in this respect. These latter have been found very useful at detecting faint structures in astronomical images. Previously we had managed to get good results for all kinds of images in shared-memory parallel machines, which works up to about 10 Gpixel or Gvoxel (I have a neat little 64 core 512 GB RAM machine at work for such chores), and on clusters for images up to 16 bits per pixel (maximum size processed to date: 165 Gpixel in under 5 minutes). The latest method can handle floating point images and volumes as well, albeit with some speed penalty. The full paper can be accessed freely here: https://research.rug.nl/en/publications/distributed-connected-component-filtering-and-analysis-in-2-d-and An earlier review on connected morphological filter for those interested can be found here: http://www.cs.rug.nl/~michael/IEEE_SPM_2009_Salembier_Wilkinson.pdf

The dioptric strength of the corrector plate is essentially zero, it is only designed to correct for spherical aberration. Of course this correction is only optimal for one wavelength, and the correction of spherical aberration is different for different wavelengths. This effect is called spherochromatic aberration, which is distinct from chromatic aberration in that there is no overall difference in magnification or focal length as a function of wavelength, as is the case in lateral and axial chromatic aberrations respectively.

Nyquist and Shannon sampling theorems assume point sampling. For square pixels, especially with high fill factor, a correction of about 10-20% is needed according to Van Vliet in his PhD thesis (somewhere early 90s, as I recall)

Nice result. I always do some deconvolution and unsharp masking in ImPPG with my solar shots. Seeing in Ca-K is often pretty bad. H-alpha is a lot easier in that respect