Author Topic: Blind deconvolution (or other superresolution systems)  (Read 628 times)

Offline joelkuiper

  • Newcomer
  • Posts: 12
    • View Profile
Drizzle integration requires well dithered undersampled images, but methods exist for recovering (perceptual) sharpness using blind (multi frame) deconvolution. These would be an absolutely amazing feature for PI, especially since lucky imaging setups with CMOS cameras (e.g. moving beyond the classical dozen or so frames into the realm of thousands) become more common.


Offline joelkuiper

  • Newcomer
  • Posts: 12
    • View Profile
Re: Blind deconvolution (or other superresolution systems)
« Reply #1 on: 2017 October 18 07:42:02 »
I've given this a little bit of thought, but as an initial pass it could be possible to:
- Extract stars and fit PSFs
- Measure PSFs, and apply a k-means clustering algorithm
- Fit a k-dimensional surface to indicate which PSF belongs to which part of the image, each dimension in k maps to a average PSF of that cluster
- Iteratively run deconvolution for each dimension (e.g. "layer") using the fitted surface as a mask

so for a dimensionality of k=5, the PSFs extracted from the image would be clustered in 5 different buckets. The average PSF of those buckets is computed. Since you know which PSFs belong to that bucket, you know the coordinates for the stars that correspond to that PSF. Then you can fit a surface (e.g. 2D surface spline) to indicate where that PSF belongs in the image. Then it's only a matter of applying deconvolution for each layer, masking out the areas that don't belong. To make sure each part of the image only gets deconvoluted once, it probably requires unit normalization on the columns. Although instead of fitting a surface for each layer, it might also be possible to segment the image, but that might introduce other artifacts I think.

... now I'm not much of a mathematician, so this might make absolutely /no/ sense … but curious about the ideas.

EDIT: Of course this is a poor mans substitute for multichannel/multiframe blind deconvolution as described in "V Zhulina, Yulia. (2006). Multiframe blind deconvolution of heavily blurred astronomical images. Applied optics. 45. 7342-52. 10.1364/AO.45.007342" (and their references to similar techniques) … but for the method above I might have a some idea on how to implement it :P 
« Last Edit: 2017 October 18 14:07:27 by joelkuiper »