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Siril 0.9.8

Release date: January 31st, 2018. Update date: February 19th, 2018.

This release is the eighth stability release for the 0.9 branch. Its SVN revision is 2085. It also has an SVN tag: 0.9.8. Stability updates and minor improvements occur in the dedicated 0.9 branch. This is the first version officially supported on Windows, and it even includes developments that were specially made for Windows. But please keep in mind that Siril is not developed with Windows and that we rely on Windows users to test and give us feedback about integration or porting problems.

However, an update (0.9.8.3) was quickly released in order to fix some minor bugs that have been found.

This update focuses on deep-sky lucky imaging processing speed operations.

Note that starting with this version, OpenCV is a mandatory dependency.

GNU/Linux

• Debian stable (stretch): 64 bits
• Debian testing: 64 bits
• Ubuntu / Linux Mint:
 sudo add-apt-repository ppa:lock042/siril
sudo apt-get update
sudo apt-get install siril

• Fedora, Mageia 6: All
• Arch Linux: All.
• AppImage: If you don't find a package for your distribution, you can try to download the AppImage binary. It has been tested on many systems and could work on yours. Make the file executable, and run it. That's all.

Windows (64bit)

We did not create an installer for it because it's a bit complicated on Windows, we only distribute the archive. We recommend you create a shortcut for the executable at any convenient place. The executable is in the subdirectory bin and is called siril.exe.

What's new in Siril 0.9.8

Siril 0.9.8 is mostly a stability and speed improvement release. As usual you can see the list of features and issues and details on the changelog page. It contains in particular some bug fixes for Windows over the previous version 0.9.7 but also adds new features:

• Subpixel alignment for stacking, or more exactly half-pixel alignment, with the feature called simplified drizzle
• Lucy-Richardson deconvolution
• Sum stacking now runs in parallel
• Processing of areas of images from SER sequences is much faster (seqpsf, average stacking)