planetary-system-stacker 0.8.32~git20221019.66d7558-1 source package in Ubuntu

Changelog

planetary-system-stacker (0.8.32~git20221019.66d7558-1) unstable; urgency=medium

  * New upstream release.

 -- Thorsten Alteholz <email address hidden>  Wed, 14 Dec 2022 23:35:45 +0000

Upload details

Uploaded by:
Debian Astronomy Maintainers
Uploaded to:
Sid
Original maintainer:
Debian Astronomy Maintainers
Architectures:
all
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section
Lunar release universe misc

Builds

Lunar: [FULLYBUILT] amd64

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File Size SHA-256 Checksum
planetary-system-stacker_0.8.32~git20221019.66d7558-1.dsc 2.4 KiB 25599cb54f1b69a6a97a499e824368ac8d75338491b8326cbf2058775b61ee5d
planetary-system-stacker_0.8.32~git20221019.66d7558.orig.tar.xz 28.8 MiB 3f9fb0c98200fb6d07f51645aa7a7e4778b3112de3fd4c8a45066fe3dec22e6a
planetary-system-stacker_0.8.32~git20221019.66d7558-1.debian.tar.xz 2.5 KiB 017ce6aaf3c2c30eecaf0298f7c0a194914929350a47c625feed11ed9d95f9aa

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Binary packages built by this source

planetary-system-stacker: create a sharp image of a planetary system object (moon, sun, planets)

 This package contrains software to produce a sharp image of a planetary
 system object (moon, sun, planets) from many seeing-affected frames using
 the "lucky imaging" technique._
 .
 The program is mainly targeted at extended objects (moon, sun), but it
 works as well for planets. Results obtained in many tests show at least
 the same image quality as with the established software AutoStakkert!3.
 .
 Input to the program can be either video files or directories containing
 still images. The following algorithmic steps are performed:
 .
  * First, all frames are ranked by their overall image quality.
  * On the best frame, a rectangular patch with the most pronounced structure
    in x and y is identified automatically. (Alternatively, the user can
    select the patch manually as well.)
  * Using this patch, all frames are aligned globally with each other.
  * A mean image is computed by averaging the best frames.
  * An alignment point mesh covering the object is constructed automatically.
    Points, where the image is too dim, or has too little contrast or structure,
    are discarded. The user can modify the alignment points, or set them all
    by hand as well.
  * For each alignment point, all frames are ranked by their local contrast
    in a surrounding image patch.
  * The best frames up to a given number are selected for stacking.
    Note that this list can be different for different points.
  * For all frames, local shifts are computed at all alignment points.
  * Using those shifts, the alignment point patches of all contributing
    frames are stacked into a single average image patch.
  * Finally, all stacked patches are blended into a global image, using the
    background image in places without alignment points.
  * After stacking is completed, the stacked image can be postprocessed
    (sharpened) either in a final step of the stacking workflow, or in a
    separate postprocessing job.