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NickK

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About NickK

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  1. Yup - most Asti images are 16bit and I found that processing you want a wider precision. Annoying most GPUs are not IEEE either.. (for my work at the time)
  2. Noisechisel paper (direct link): https://arxiv.org/pdf/1505.01664.pdf interestingly I already use the psf to detect local noise and reject it.
  3. Just had an idea wrt burred signal and if the noise vs time can be used. not only for deconv, super resolutiob but noise signal analysis at the same time. just need time to think through some maths.
  4. https://www.sciencealert.com/this-enhanced-image-has-allowed-us-to-peer-deeper-into-space-than-ever-before This looks interesting as a technique.. if you dig into to process; it’s described in this paper: https://arxiv.org/pdf/1810.00002.pdf
  5. Hi, Sorry for the delay responding - just back yesterday. I'll have to have a look again when I have time. I hope soon. Nick
  6. Heres the swarm based paper: http://www.inf.ufpr.br/vri/alumni/2013-PeterPerroni/GPU-PSF-Estimation-with-CPSO-dissertation.pdf
  7. I've bust both a nVidia and this one, a non-retina ATI GPU - basically Apple's design isn't good enough to cope with severe workloads vs the "high" workloads that people normally create. It was nvidia at the time - a university paper exists that demonstrates the brute force mechanism to reverse engineer the psf. It was essentially using a PSF vs a known PSF of stars over the image. I think there could be some optimisations - I've done IIR filters by pole fitting the PSF to a single pixel. Although a simple gather works better for non-symmetric, the IIR for a 2D image and 2D PSF was stupidly quick.
  8. What is funny is that AI deep style networks and normal image filters aren't that far apart. They both use what is effectively kernels. I've done a fair amount of both. I've always get the feeling that Topaz oversell aggressively in their adverts, almost in the same scenario where the company is over-compensating. My favourite processing is where swarming implemented in GPU was used to back propagate to define the PSF at a number of key points then using interpolation between the PSF across the image, the PSF deconvolution is applied - and a final deconvoluted sharp image appears. The disadvantage of this solution is it take hours and hours of 100% GPU time.. Tempted to buy a seperate small tower computer and put a couple of GPUs for experimentation as I've bust one GPU due to heat in my MacBook Pro. However the other half has plans too..
  9. Oversaturation does mean you loose measured information, just loading the unstretched PSF into PI & the image, channel splitting, running seperate RGB deconv, then recombining (loosing some detail in png) but looking carefully I think it could work but I think trying to source from PNG has already killed it:
  10. I've already implemented this already - one fast option that works with GPU is phase correlation. Do 2D FFT on the image and the PSF, then do a image wide phase correlation. There will now be a pixel by pixel map - look at the phase correlation for the suspected hot pixel and volia! In the case below I'm using the guide start as the PSF rather than gaussian. Here: If you look at the first image - at the top you see noise (hot pixel), if process you get without using PSF phase correlation to detect the local noise you get image 2 where you can see noise processed into the image.. then with psf phase correlation noise removal you get the 3rd.. the 4th is an image from aladin.fr to demonstrate what is noise vs signal. In this scenario I use a quick detector to find the suspects then apply as needed for speed.
  11. Nice use of convolutional networks to remove point noise: https://arxiv.org/pdf/1612.04526.pdf although this can also be done against the PSF (ie point noise appears not affected by the same PSF that affects the rest of the image).
  12. A nice summary of nice - including this depth of focus using coded apertures. http://kiss.caltech.edu/workshops/imaging/presentations/fergus.pdf Which got me thinking ... if we use a coded aperture, could we remove near field issues such as atmospheric distortion? If we can detect the ranges of objects we could reject those outside of the range over time, thus we could then remove the error. For lunar images - this would be interesting.. using a split prism, it could be possible to have one camera with a coded aperture to estimate the error, the main camera then produces the image. Post processing then delivers the corrected image. For deep space - the image is at infinity, thus coding outside of this indicates non-deepspace data.
  13. You could use that stacked raw PSF and deblur images affected with it...
  14. Careful - the drv8825 will take more voltage, less current. I use a 3.8V 650mA stepper with 100:1 gearbox but use a 12V supply. Set correctly the 8825 chops the current automatically so you get no heat in the stepper and the 12V step overcomes back EMF.
  15. Hi, Just had a look at the code whilst I have a biscuit and drink break. I see you're using SLEEP pin by default: power to the H bridges is cut so if the load requires the current rather than friction to hold that could mean the the focuser moving especially with momentum generated by speed with mass. re-enabling after a sleep, although it doesn't reset the microstep count in the driver, the stepper motor itself may step to the next winding (ie 1/2 or full step) there's a 1ms delay before the DV8825 before the stepper driver is ready to take any instructions such as a STEP operation, there's a 250us delay using operations in call in step() before attempting the step in the 4988 driver. In the end the DRV8825 doesn't get that hot, even when micro stepping so it may be better defaulting to not sleep. It should be an easy exercise to create the 8825 from the 4988 by a subclass and override of a delay method.. (or a little refactoring to make a DRV base class for both drivers).
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