Recent Articles in PixInsight Horizons

The fun part is using Image Integration to see the data in its full form for the first time. GUM 15 does not disappoint.

In this lesson hot pixels are removed with Cosmetic Correction and the images are registered with StarAlignment. 

This first lesson on GUM 15 introduces data and evaluates its quality. 

Occasionally PCC will not work for a number of reasons. This lesson demonstrates one such example. Often with a tool this complicated it is difficult to know where to start to solve a problem. Luckily in Pixinsight the Console is quite verbose for many processes including PCC. Just a little reading quickly points to the solution. 

This lesson demonstrates a method (actually a few) that minimizes the color fringes that are sometimes seen around stars. Both the newly improved ImageSolver and new MaskGen script are demonstrated as well. 

This Lesson is available to PixInsight Horizon members.

In this lesson Adam demonstrates what to look for in your integrated results and how to potentially improve them by adjusting the appropriate parameters. (This is somewhat vague since the lesson demonstrates the idea through a typical route of discovery.) Few tutorials explain how Image Integration works in detail (see Pixinsight Fundamentals)- fewer yet show concrete examples of optimizing results. 

In this demonstration Adam shows one way to combine some old data with newer observations. This straightforward presentation tackles a few small hurdles that are in themselves instructive. 

This section takes a thorough look at MURE Denoise. The section on Gain and Readnoise (Fundamentals) is a prerequisite for this lesson. This method of noise reduction is truly underappreciated- it is a remarkable tool with very pleasing results. 

This lesson explains the basic idea behind drizzling undersampled images and then demonstrates a few examples. Subtle and neat to do! Look beneath the video for additional notes.

This video is part of the PI Edition of the Cosmic Canvas. It is available to all PI Horizons members. This is Part 1 of 8 sections which show how to process a very short data set into something (hopefully) attractive. 

The Cosmic Canvas is Adam's "Bob Ross" like delivery of a complete processing session starting from scratch and producing a final rendered image. 

This is a second example of the use of Local Normalization. It is a bit more subtle than the first example and the demonstration deals with attending to smaller scale issues. This second was made as a way to compare the results of selective DBE (placing samples on problem areas) with what Local Normalization does with the same data. 

This lesson shows an excellent example of the use of Local Normalization to correct for non-uniformity of illumination due to various things (in this case high thin clouds). 

This short lesson demonstrates a common method of dealing with objects that have a very bright central region and everywhere else in the frame is faint. Although the creation of an exact luminance mask is possible- it often requires many more steps and time. In this particular case the method is well-suited since any luminance errors due to masking are not critical. 

This video is featured in the PixInsight Fundamentals workflow section- but it deserves to live here as an independent video. The technique demonstrated can work on various kinds of small glows and dust motes from poor flats. 

This is a members-only video protected by copyright (Clear Skies Astronomy LLC).

This is a members-only video lesson protected by copyright (Clear Skies Astronomy LLC).

This is a members-only video protected by copyright (Clear Skies Astronomy LLC).