Image Integration

ESD is now my favorite pixel rejection method! I explain the math behind the magic in this section so that you can feel confident using it as well. It has not be a part of PixInsight for very long- but in my trials so far- I have not seen any downsides to its use. 

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 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.