Outcome of our PixInsight Workflow Course for NGC3324 Cosmic Cliffs JWST raw data.
https://www.kozmosi.io/funnel/ngc3324Motivation for the course: Being new to PixInsight it takes a long time to get up to speed and to understand how to process images in a productive way. Currently there are few resources that show you the entire workflow in one place from Installing PixInsight, Importing Raw Data, Linear Processing, Non-Linear Processing, Pos-Post processing and exporting for web and print.
Course Content: The course contains
18 Workflow Chapters across 4 sections, with
33 Bonus Chapters Detailing every PixInsight Process and Script used throughout the entire course. In total there is about
15 hours of video content and
51 Astro-Files one for each chapter that includes a step-by-step workflow process downloadable PDF.
Project Setup Section Chapters- Introduction: In the introduction, we will cover how to get the most out of the course, the resources available to you and the general structure of the steps we will be taking in order to create our final image.
- Project Structure: Once we have PixInsight installed and ready to run, we will then go over how to create different project structures and how to create a template project, this template project will help us save time when we are doing future image processing and will take us out of the amateur league and into the professional league.
- Raw Data: In this chapter, we will learn how to download our raw deep space image data absolutely free, thanks to the amazing MAST database. The files available from the MAST database can be extremely large so being able to download exactly what we require is essential.
- Data Import: In the final chapter of the project setup section, we will import the raw data files into PixInsight and learn about the FIT file format used by telescopes.
Linear Processing Section Chapters- Star Alignment: In the first chapter of the linear processing workflow section, we will be fixing the alignment issues between the different raw data files we are processing, this will make sure we do not end up with a blurry image. We will create a structure map for our reference image using the star alignment tool which will produce star aligned registered images.
- Framing: In the Framing chapter, we will be using tools to flip and crop our images so that the nebula is positioned similar to our reference images from the James Webb Space Telescope Gallery.
- Star Core Fix: In this chapter we will learn a repeatable process to fix blow out star cores. Some larger stars have cores that are blown out or clipped in the original James Webb Space Telescope data this clipping is caused by the filter collecting more data than the shorter wavelength filters until the clipping occurs due to too much data. However, we need these longer wavelength filters in order to see the finer detail.
- Linear Fit: Each detectors filter throughput are not equal, the response of each pixel to the wavelengths, may differ for the same exposure time and the resulting image intensity for each filter will often differ. This difference in image intensity across channels will usually result in some kind of color cast or even a strong color bias when the channels are combined. In this chapter we will linear fit our filter data to rectify these issues.
- Combine Images: In the last chapter we combined our filter images using PixInsights Pixel Math Tool into a single grayscale image that used each images pixel data as an equal weight in the final image. Our challenge is going to be how to combine these 6 filter images in the correct proportions of the three primary colors available to us, that would give us a good representation from which to start further processing.
Non-Linear Processing Section Chapters- Image Stretching: We now have our combined color image, however, we have been working with the data in its linear form so that we could fix the problems in the original files, such as the blown out star cores. For the remainder of the processing we are going to do, we need to convert our image into the non-linear form as we want to work on the nebula and other fainter artefacts that are not as visible in the linear data.
- Star Extraction: We need to be careful not to affect the stars in the image when we are trying to accentuate the details in the nebula. We are at risk of over-exposing the brighter objects in the image, in order to prevent this we can extract out the bright objects from the background and work on the two independently.
- Color Masks: In Astrophotography we can use color to differentiate between items in an image to bring them to the
attention of those viewing our images. In this chapter we will apply color masks and use the curves transformation tool to bring out the color details in our image.
- Luminance Mask: Our image is still looking a little flat, so we will be using a luminance mask in this chapter to
apply a greater strength mask to high signal areas and lower strength to low signal areas. Luminance masks are used many times during the image processing workflow and are helpful with initial noise reduction, color saturation and brightness adjustments.
- Local Histogram: In this chapter we will use the Local Histogram Equalization process to increase the contrast in the nebula. We will need to be careful not to over-process the image as the Local Histogram Equalization process can create artefacts such as halos or unnatural color gradients when applied aggressively.
- Dark Structure Enhance: With the contrast in our Nebula enhanced, we are able to see much more detail in the dust lanes. Our next challenge is to get the darker structures in the image to stand out from the background Nebulosity. Using a process called Dark Structure Enhance, we can make the darker structures in the image stand out more clearly.
- Re-Combining Stars: Now that we have finished processing the background of our image, we need to bring our attention to the star field. In this chapter we will fix both the green and magenta tints to our stars, once we have finished this recalibration we will merge our stars back into our background nebula image for further processing.
Post-Processing Section Chapters- Watermarks: Now that we have finished processing our image, we can add a watermark so that we can share our image.
If we do not add a watermark then it would be difficult for us to know where our images are being shared or displayed on the web. You can use any image as a watermark, your logo or even your own signature.
- Exporting: In this chapter, we will discuss PixInsight's Color Management Setup, How to convert between color profiles
and resample the sizes of images depending on their intended use, be it for print or web.
33 Bonus Chapters covering all processes and scripts used in the course. - PixInsight Installation: PixInsight is an extremely powerful piece of AstroPhotography processing software. In this bonus chapter we will discuss the benefits of using PixInsight along with how to install a FREE trial version which is more than enough time to finish this course.
- User Interface: PixInsights user interface can be confusing to new users, in this bonus chapter we cover everything you need to know to get up and running with PixInsight.
- File Explorer: The File Explorer in PixInsight has advanced features for working with Astronomical Images, in this bonus chapter we dive deep into the capabilities and benefits of using the custom File Explorer.
- Screen Transfer Function: The STF is one of the most essential processes we use when editing astronomical image data. It enables us to edit data in its linear form whilst still being able to view our changes. We introduce this process early in the workflow as we need to get proficient using it.
- Process Explorer: This feature-rich tool offers a multitude of functionalities that make it a valuable asset for both beginners and advanced users. In this Chapter, we will delve into the main features of the Process Explorer and discuss why it is such a useful tool in PixInsight.
- Star Alignment: The StarAlignment tool in PixInsight is a powerful feature designed to align multiple images of the same celestial object. Its primary purpose is to accurately align the individual frames so that we can create a final stacked image with improved signal-to-noise ratio,
enhanced details, and reduced noise and artefacts.
- Dynamic Crop: The Dynamic Crop tool in PixInsight is a powerful feature that allows us to selectively crop and resize our images while maintaining full control over the cropping parameters.
- Fast Rotation / Geometry: The FastRotation process implements orthogonal and specular geometric transformations: This means we can rotate 180 degrees, rotate 90 degrees clockwise, rotate 90 degrees counter-clockwise, horizontal mirror and vertical mirror. These operations are collectively known as fast rotations on the PixInsight platform.
- Preview Windows: Preview windows in PixInsight are interactive image display panels that provide real-time previews of image processing operations. They allow us to visualize the effects of different processing steps and settings before applying them to the entire image.
- Propagate Previews: The Propagate Previews script in PixInsight is an incredibly useful tool that simplifies and accelerates the process of generating and managing previews in a PixInsight project.
- Range Selection: The Range Selection process in PixInsight is a powerful tool that allows us to generate a mask by defining a range of pixel values. Those pixels whose values fall within the selected range will be rendered as white pixels on the mask, while pixels outside the selected range will be rendered as black mask pixels.
- Masks: Masks allow us to selectively apply adjustments, enhancements, or corrections to specific areas of an image while leaving other areas unaffected. In this bonus chapter we will introduce several types of masks and how to apply them to our image workflows.
- Multiscale Linear Transform: The MultiscaleLinearTransform is a powerful tool in PixInsight that is widely used for image enhancement and noise reduction. It operates in the multiscale domain, allowing for selective manipulation of different scales or levels of detail within an image.
Morphological Transform: The Morphological Transformation process allows for the enhancement of specific features in an image by applying morphological operations such as dilation, erosion, opening, and closing. These operations can emphasize or amplify certain structures or shapes, making them more visible and prominent.
- Pixel Math Dialog: PixInsight's PixelMath is an extremely versatile and powerful tool to perform pixel-level arithmetic and logical operations between images.
- Process Icons: Process icons are special process containers that can exist as independent objects on the workspace. A process icon encapsulates a process instance together with an identifier, a graphical interface, and a set of functions to create, destroy, save, retrieve and modify it.
- Linear Fit: The LinearFit process is used for photometric calibration, which involves scaling and matching the intensities of images to a reference image.
- Noise Evaluation: The Noise Evaluation script in PixInsight aids in the assessment of noise in digital images. This quantitative analysis is crucial for evaluating the effectiveness of noise reduction methods and optimizing processing workflows to achieve desired noise levels while preserving image details.
- Statistics Panel: The Statistics Panel in PixInsight is a powerful tool that provides valuable information about the statistical properties of an image including essential statistical measurements such as minimum, maximum, mean, median, standard deviation, biweight midvariance, and many more.
- Script Repositories: Script repositories are invaluable resources for PixInsight users, providing a multitude of benefits that enhance PixInsight's functionality and streamlines image processing workflows.
- Combine Images Script: PixInsight has the Channel Combination tool for combining up to 3 pixel channels or color space components into an RGB color image. We will install a custom script to allow us to combine up to 7 images.
- Linear vs Non-Linear Data: When processing astronomical data, we need to understand the difference between linear and non-linear data so that we can apply processing steps to the data in the format that will have the most impact.
- Histogram Transformation: The Histogram Transformation Tool allows us to adjust the dynamic range of an image by redistributing its pixel values. This is crucial for optimizing the overall contrast, brightness, and tonal balance of an image.
- StarNet2: We will install an open source third party adversarial network called StarNet, this tool will automatically extract out the stars from our image so that we can work on the background and stars in dependently.
- Color Mask Script: In this chapter we will provide details of both the original Color Mask Script and the modified version of the Color Mask script.
- Curves Transformation: The Curves Transformation process in PixInsight is a powerful tool for adjusting the tonal range and contrast of an image. It allows precise control over the mapping of pixel values, enabling us to enhance or correct the overall brightness, contrast, and tonal distribution of an image.
- Multiscale Median Transform: The MultiscaleMedianTransform is a powerful tool in PixInsight that is primarily used for reducing noise in an image while preserving edges and fine details. The MultiscaleMedianTransform decomposes the image into multiple scales or layers, but it employs a median-based transformation instead of the linear transformation used by the MultiscaleLinearTransform.
- Local Histogram Equalization: The Local Histogram Equalization processes main objective is to enhance the local contrast and visibility of structures, especially in regions of the image with low contrast. The process is specifically designed to operate on non-linear, already stretched images, making it a valuable tool in enhancing details and contrast in various types of images.
- Dark Structure Enhance: The Dark Structure Enhance script in PixInsight is a valuable tool designed to enhance faint and intricate structures present in dark regions of astronomical images. It is particularly useful for images of deep space objects where faint nebulosity, dust lanes, and intricate details can be challenging to bring out without introducing noise or artefacts.
- SCNR: The subtractive chromatic noise reduction process (SCNR) in PixInsight is a powerful tool used to effectively remove chromatic noise from astronomical images. Chromatic noise, also known as color noise, is a type of noise that appears as unwanted color variations in an image. The subtractive chromatic noise reduction process is especially useful for Astrophotographers who capture images using color imaging sensors, as it helps to eliminate unwanted color artefacts while preserving the fine details and colors of the astronomical objects.
- Color Management Setup: The Color Management Setup process in PixInsight is a crucial tool that allows us to configure and manage color settings for our images. Color management is essential for maintaining accurate and consistent colors throughout the image processing workflow, especially when working with images from different sources, cameras, or displays.
- ICC Profile Transform: When working with images from various sources, they may have different color profiles embedded.By using the ICC Profile Transformation process, we can convert images to a desired color space, ensuring consistent color representation and maintaining the image's color integrity throughout the processing workflow.
- Resample: The Resample process in PixInsight is a fundamental geometric tool that serves the important purpose of resizing target images. It offers several pixel interpolation algorithms to ensure the best quality during the resizing process.
- Crop: The Crop process in PixInsight is a fundamental and versatile geometric tool used to adjust the dimensions of a target image. It offers various functionalities to add or remove margins of user-defined lengths, making it possible to modify the image's size and composition according to specific requirements.
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