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382 points DamonHD | 3 comments | | HN request time: 0.318s | source
1. bob1029 ◴[] No.43698050[source]
It would seem techniques like this have been used in domains like astronomy for a while.

> The reconstruction of objects from blurry images has a wide range of applications, for instance in astronomy and biomedical imaging. Assuming that the blur is spatially invariant, image blur can be defined as a two-dimensional convolution between true image and a point spread function. Hence, the corresponding deblurring operation is formulated as an inverse problem called deconvolution. Often, not only the true image is unknown, but also the available information about the point spread function is insufficient resulting in an extremely underdetermined blind deconvolution problem. Considering multiple blurred images of the object to be reconstructed, leading to a multiframe blind deconvolution problem, reduces underdeterminedness. To further decrease the number of unknowns, we transfer the multiframe blind deconvolution problem to a compact version based upon [18] where only one point spread function has to be identified.

https://www.mic.uni-luebeck.de/fileadmin/mic/publications/St...

https://en.wikipedia.org/wiki/Blind_deconvolution

replies(1): >>43698723 #
2. dopadelic ◴[] No.43698723[source]
This makes sense for blurring, but not for pixelation mosaicking.
replies(1): >>43698938 #
3. gliptic ◴[] No.43698938[source]
For pixelation you can use another technique invented for astronomy: drizzling [1].

[1] https://en.wikipedia.org/wiki/Drizzle_(image_processing)