Astrophotography: Processing Galaxies with PixInsight

Get over the hurdle of PixInsight’s complexity and learning curve, and apply it to make your galactic astro-images awesome.

PixInsight is a powerful tool, but with a steep learning curve. If you’re ready to take your astrophotography to the next level, this course will de-mystify PixInsight and unleash its potential for you. Even if you’re an experienced PixInsight user, I think you’ll find some tips, tricks, and scripts you may not have encountered before.

We’ll walk through the processing of the galaxy M61, a distant, challenging target that includes an image of a recent supernova! As we go, we’ll cover:

• Installing my PixInsight process icons and third-party scripts to simplify things
• Identifying and discarding bad sub-frames with the Blink process
• Calibrating your frames across multiple nights using the WeightedBatchPreprocessing script
• Weighted stacking with WeightedBatchPreprocessing
• Linear noise reduction with MURE Denoise
• Cropping your integrated images
• Creating luminance, range, and star masks
• Sharpening your image with deconvolution
• Background extraction
• Color combination, luminance integration, and photometric color correction
• Stretching into non-linear space while preserving star colors
• Nonlinear noise reduction with TGVD Denoise
• Aesthetic tweaking with RGB and saturation curves
• Shrinking and re-shaping stars with MorphologicalTransform
• Annotating your images
• Final tweaks in Photoshop

This walkthrough uses monochrome data collected over 3 nights using luminance, red, green, and blue color filters. But many of these techniques will be the same if you’re using a color camera as well.

Don’t let PixInsight’s unusual user interface and complexity scare you off – it’s worth learning, and can really take your images to the next level.

Course Instructor

Frank Kane Author

Our courses are led by Frank Kane, a former Amazon and IMDb developer with extensive experience in machine learning and data science. With 26 issued patents and 9 years of experience at the forefront of recommendation systems, Frank brings real-world expertise to his teaching. His ability to explain complex concepts in accessible terms has helped over one million students worldwide gain valuable skills in machine learning, data engineering, and AI development.