Recent Articles in Processes

The lesson highlights the parameters and features of MLT. Knowledge of the previous lessons, both in this category and elsewhere, give us a way to interpret the various sections of the process.

In this lesson a basic understanding of Fast Fourier transforms is presented. It is mostly a visual demonstration of the important mathematical properties of FFTs that lead to an understanding of Wavelets. 

This is the first introductory lesson that explains some basic ideas of signal processing using cyclic functions (Sine Waves). We live in a universe that is comprised of waves. It should be no surprise then that we can model astronomical images and other phenomena using them!

This lesson demonstrates the basic usage of the DynamicPSF process in PixInsight. Some explanation of data modeling is mentioned here. However, review of the Image Integration Primer material will help as there is a specific example that uses DynamicPSF. 

In this final lesson on Image Integration each image type (Biases, Darks, Flats, and Lights) is considered along with the parameters that are used to generate an integrated image. This is usually the section you will find in most tutorials, but only the previous content really explain the material and put it into context. 

This lesson covers the different output normalization methods available in Image Integration. Noise evaluation is also discussed as it relates to improving the integrated results. 

PixInsight provides a number of different outputs to analyze the results of pixel rejection. These maps and console results are discussed in this lesson. In addition both the Large Scale enhancement and Range rejection features are shown.

After normalizing the data Pixel Rejection occurs using a handful of methods that are highlighted in this lesson. Rejection Maps, Slope Maps (Linear Fit Rejection) and the console output are all investigated as well.

Here we take a deep dive into PixInsight's method of normalizing images by use of statistical image properties. The dispersion of images, called scale, is calculated and used to make the images statistically compatible for pixel rejection (and later output if desired).

This lesson introduces the Image Integration process by describing what it means in the "real world"- i.e. achieving the best estimate of contrast from pixel to pixel. This is what makes an image! The Image Integration process has a few working parts that are described at a high level.

In this final installment of the primer, we look at the most common measure- the Standard Deviation of a set of values and how it is useful to identify outliers.

In part 5 of this primer on fluctuations and statistics, a real world example adapted from Adam's "other job" is shown. Then, in a relatively rare for tutorial bit of content, the Dynamic PSF process is explained based on the knowledge built up from the previous sections.

In Part 4 of this series of lessons we finally delve into the measures of distributions that are typically found in PixInsight: including the Average Absolution Deviation about the Median and MAD. 

This lesson begins the introduction to the analysis of a set pf values by using the mean, median and a histogram. 

A continuing lesson on light and Poisson distributions. 

This video is a primer for concepts that wend their way throughout the PI Universe. Fluctuations of measurements and the very nature of light play a critical role in how images are analyzed and data is processed. 

Star Alignment in PixInsight is the main process used for image registration. Under the hood there is a good deal of complexity- however in terms of usage the default settings usually work making this step of image processing one of the simplest to perform. Thus this section highlights important concepts such as geometric transformations and interpolation methods which are important for image registration and other areas of image processing. 

This video is a lesson on the use of TGVDenoise- one of PixInsight's super tools!

This lesson demonstrates the usage of ArcsinhStretch- a process designed to manage color fidelity (and saturation) while brightening an image.

In this lesson, until otherwise persuaded/corrected, I rail against the sometimes used technique of "balancing" RGB data through the use of LinearFit. I explain how/why it somewhat works- and where it fails.