Processing in depth

Alignment and integration

Reference images

AutoIntegrate automatically selects the reference images for star align, image integration and local normalization. By default the images with the best SSWEIGHT value are chosen.

It is possible to manually choose reference images. A reference image for star alignment is used to align all light images. For each filter reference images are used for image integration and local normalization.

There is a button that can be used to calculate SSWEIGHT values for each file and automatically mark reference images to the file list. It is useful for checking which files would be chosen and fine tune the selection if needed. SSWEIGHT values are calculated for the files in file list that may not debayered or cosmetically corrected so they may differ from those calculated during processing. If no reference images are selected manually then automatically selected images are marked to the file list after processing. They can be found also from the AutoIntegrate log file.

Note that manually selecting reference images for filters do not work with OSC images if the Extract channels option is used. In that case extracted channel images should be loaded separately as LRGB images to choose correct reference images.

FastIntegration

AutoIntegrate supports the FastIntegration process. FastIntegration is a new process that is available in PixInsight version 1.8.9-2 or later. It is a faster version of the old ImageIntegration process. It is recommended to use FastIntegration instead of ImageIntegration if you want quick results or if you have a huge number of images.

FastIntegration has some setup options in Integration / FastIntegration section. By default FastIntegration skips cosmetic correction and also uses a subset of frames to find the reference image. It is possible to change these settings in the FastIntegration section.

FastIntegration also supports drizzle integration. Drizzle integration is useful when you have undersampled data and you want to increase the resolution of the image. Note that drizzle integration uses the normal DrizzleIntegration process so some performance benefits of FastIntegration are lost.

Crop to common area

It is possible to automatically crop bad pixels on the sides of the images. This works by integrating all images, finding an area where all images are contributing and cropping channel images to that area.

Automatic cropping makes it easier to get an image with only good data in it. It also helps automatic processes such as stretching because only real data is included in the image.

To check how much cropping was an image file LowRejectionMap_ALL is left minimized on the screen.

Automatic cropping can be used also during AutoContinue. Cropping requires that the LowRejectionMap_ALL file is open in the PixInsight desktop. Cropping can be done only if starting from Integration_[LRGBHSO] or Integration_RGB_color files.

In the case of AutoContinue it is possible to edit the crop preview area in the LowRejectionMap_ALL image. AutoContinue will use the crop preview from LowRejectionMap_ALL. In this way it is possible to rerun the processing using a different crop if needed.

Below is an example that shows the difference between non-cropped image (left) and cropped image (middle). On the right is a LowRejectionMap_ALL file showing the cropped area.

Sometimes the cropping also helps with automatic processing. Below is an example that shows the processing difference between non-cropped image (left) and cropped image (right). Since bad pixels on the side are cropped, automatic processing can better stretch the images since there is only real data in the image.

Linear processing

Background neutralization

Option Use BackgroundNeutralization can be selected to run the background neutralization process automatically.

For background neutralization a method is added to find a true background area in an image. True background area is then used by the BackgroundNeutralization process. A new image named AutoBackgroundModel is created that shows the background area. The AutoBackgroundModel can be edited and it is used with AutoContinue if it already exists.

Finding a true background is enabled by default but can be switched off with the No auto background option in the Other / Other parameters section.

Gradient correction

Selecting gradient correction process

Multiple options for gradient correction are available. The default option is to use the GradientCorrection process.

There are options to use the following processes for gradient correction.

• GradientCorrection (default unless some other option is selected)
• ABE (AutomaticBackgroundExtractor)
• DBE (DynamicBackgroundExtraction)
• MultiscaleGradientCorrection
• GraXpert

To try out different gradient correction methods there is a standalone script Gradient Correction in the Script / AutoIntegrate menu.

Gradient correction is selected in the Settings / Tools section.

Notes on different gradient correction methods:

• When DBE is used, sample points are set automatically for the image.
• If GraXpert is used, it must be installed separately as it is an external tool.
• When using MultiscaleGradientCorrection you need to set up the MARS database in PixInsight. Note that the MARS database does not yet cover the whole sky. If the image area is not in the MARS database, normal GradientCorrection is used. Also the image must be plate solved when using MultiscaleGradientCorrection.

For setting the parameters for the gradient correction processes, see Postprocessing / Gradient correction, ABE settings, DBE settings section and Tools / GraXpert section.

When to run gradient correction

There are options to select when the gradient correction is executed. The default option is not to do gradient correction. One of these options must be selected to do gradient correction. Also multiple options can be selected at the same time.

• Gradient correction on channel images
• Gradient correction on combined images
• Gradient correction on stretched images

At which point of the processing the gradient correction is executed is selected in the Settings / Image processing parameters section.

Exclusion area for DBE

When using DBE it is possible to exclude an area for selecting sample points. This can be useful for example when there is nebulosity with low brightness in the image. Exclusion area is used also when selecting background sample for background neutralization.

Exclusion area is set in the Postprocessing / Gradient correction, ABE settings, DBE settings section. It opens a new dialog where you can select the exclusion area. From the drop down list is is possible to select the image that is used for selecting the exclusion area. If the image is non-linear it is auto stretched for the exclusion area dialog.

If images are not well aligned, it is recommended to use the Settings / Image processing parameters / Integrate only option. This will align the images before the exclusion area is selected. Autocontinue can then be used to continue the processing.

Color calibration

By default color calibration is done using the ColorCalibration process. It is run on the linear RGB image before the image is stretched to non-linear.

SpectrophotometricColorCalibration

Optionally SpectrophotometricColorCalibration (SPCC) can be used for color calibration. Option Color calibration using SPCC is used to enable SPCC. SPCC is run on linear RGB image. Some configuration options are available for SPCC.

SPCC requires that the image is plate solved. Plate solving is run automatically on Integration_RGB image which is also saved to disk. Some sources say that you should use Drizzle with scale 1 or 2 when using SPCC.

Sample narrowband color calibration output

Below is an image that shows results from different narrowband processing using different color calibration and AutoSTF auto/linked/unlinked channel options.

The first row is using defaults with no color calibration and auto (unlinked) channels for AutoSTF. Second row is using SPCC with auto channel link mode. With narrowband images and SPCC the auto channel link mode AutoIntegrate uses linked channels for AutoSTF when there is H assigned into the red channel, otherwise AutoIntegrate uses unlinked channels.

Image solving

If the image does not have center coordinates for plate solving then coordinates can be given in a new Postprocessing / Image solving section. Coordinates can be given manually or searched using an online database (Sesame). Image solving needs correct focal length to work properly. If focal length is not embedded into image metadata it can be given in the Focal length field.

Noise reduction

Option Auto selects automatically correct time for noise reduction. It is the default option. Noise reduction is done on the processed linear image, that is, on the combined RGB or color/OSC image and on the luminance image. It is done after possible deconvolution with BlurXTerminator or GraXpert deconvolution and before the image is stretched to non-linear. Noise reduction is done on the combined image and not on the individual channel images.

Option Channel image does noise reduction on each color channels and luminance image separately. This option does nothing with color/OSC images.

Option Combined image does noise reduction on combined image. Image can be from channel combination or from integrated color/OSC image. On L image noise reduction is done before processing which is the same as channel noise reduction.

Option Processed linear image does noise reduction on processed RGB image and possible luminance image in linear stage.

Option Non-linear image does noise reduction in non-linear state after stretching on combined and luminance images.

Noise reduction should be done after BlurXTerminator or GraXpert deconvolution. Option Auto does that, other possible options are Processed linear image and Non-linear image. Note that option Combined image does noise reduction before deconvolution. But it is always good to experiment what is best for your own data.

By default noise reduction uses MultiscaleLinerTransform. There are also several AI tools available for noise reduction: NoiseXTerminator, GraXpert denoise, DeepSNR or MLDenoise.

MLDenoise is a noise reduction process that is included in PixInsight starting from version 1.9.5 so it does not need to be installed separately. It is selected in the Settings / Tools section.

MLDenoise needs a model file that must be downloaded separately. Model files have a .xmlm extension and they can be saved to any directory. The model file is set in the Tools / MLDenoise section. If the model file is not set when MLDenoise is selected, the script asks for it. The model file must be given in AutoIntegrate, setting it in the PixInsight process does not suffice.

The amount of noise reduction, high-precision inference and local support are set in the Postprocessing / Noise reduction section. Other MLDenoise settings use the default values.

Options for different noise reduction tools.

Stretching

Image stretching

AutoIntegrate offers a few options to stretch an image from linear to non-linear.

• Auto STF - Uses Auto Screen Transfer Function.
• MultiscaleAdaptiveStretch - Uses MultiscaleAdaptiveStretch.
• Masked Stretch - Uses MaskedStretch to stretch an image.
• VeraLuxHMS - Uses VeraLux Hypermetric Stretch to stretch an image.
• Masked+Histogram Stretch - Uses MaskedStretch with Histogram prestretch to stretch an image.
• Histogram stretch - Using simple histogram transformation to get histogram median or peak to the target value.
• Other stretch methods

To try out different stretching methods there is a standalone script Image Stretching in the Script / AutoIntegrate menu.

Auto STF

Auto STF is the default stretch. It is good in most cases. It can be fine tuned with STF targetBackground parameter. If you get too bright an image you can try lowering targetBackground value.

MultiscaleAdaptiveStretch

MultiscaleAdaptiveStretch is the new a new built in stretching tool in PixInsight.

Masked Stretch

Masked Stretch is a good choice when Auto STF does not give a good image. For example with bright galaxy images Masked Stretch is a good choice as it keeps brightness in control and gives better saturation.

VeraLuxHMS

VeraLuxHMS can work well on many targets. Typically it is great if you want saturated results.

VeraLux HyperMetric Stretch is a new stretching method created by Riccardo Paterniti. His original implementation is done in Python for Siril. This new stretching looks very interesting and has been added to the AutoIntegrate script.

The AutoIntegrate implementation was done using the original Python version that was found from GitLab. It was converted to the PixInsight Javascript using Claude AI tool.

Masked+Histogram Stretch

A minimal Histogram based stretch is done before Masked Stretch. This can help with stars that can be too pointlike with Masked Stretch. There is a separate parameter for the histogram prestretch target.

Histogram stretch

Histogram stretch is a very simple stretch that moves the histogram median or peak to the target value. Stretch is done iteratively in small steps.

Histogram stretch works best with images that are processed with the Crop to common area option.

Other stretch methods

There are a few other stretch methods available. These could be described as experimental. They may not be as useful as other stretch methods but they can be helpful in some special cases.

In the Postprocessing / Stretching section there are some settings for the stretch methods.

Stars

Removing and stretching stars

AutoIntegrate can automatically remove stars from an image. There are a few options to remove stars and combine them back into the starless image. Starnet2 or StarXTerminator can be used to remove stars. Star removal and stretching options are available in the Postprocessing / Star stretching and removing section.

A separate stretching is used for the stars image. Stars image stretching and combining settings are selected using the Stretching for stars and Combine options.

A separate RGB stars section has an RGB stars option that is used to create RGB stars for narrowband images. For more details see Automatic processing of RGB stars in the narrowband guide.

Steps to remove stars.

• In the Settings / Tools section choose either StarXTerminator or StarNet2 for star removal.
• In the Postprocessing / Star stretching and removing section select when to remove stars.
• Run a normal processing.

In the end there are three images.

• AutoRGB has stars being added back.
• AutoRGB_starless is the starless image.
• AutoRGB_stars is the star image.

Remove stars from channel images

With LRGB and narrowband images this option removes stars from L, R, G, B, H, S and O channel images separately before channels are combined and while images are still in the linear stage. Star images from channels are then combined to create a star image.

With color images (DSLR/OSC) the option removes stars after color calibration while the image is still in the linear stage.

Remove stars before stretch

Removes stars from combined RGB or narrowband images just before stretching while it still is in the linear stage. Stars are used only from the RGB/narrowband image, stars from the L image are not used.

For OSC data this may not work well. Separating channels might help.

Remove stars after stretch

Removes stars from combined RGB or narrowband images just after stretching when the image is not any more in linear stage. Stars are used only from the RGB/narrowband image, stars from the L image are not used.

Remove stars from light images

This option removes stars from each individual light image. Star removal is done after star alignment and before optional comet alignment.

Unscreen stars

For both normal and enhancements processing there is an Unscreen stars checkbox. This option uses a bit of a different method to generate the stars image as described by Russell Croman. For details see this post: "Unscreening" and re-screening: recombining stars with starless images

The Unscreen method usually keeps star colors more correct than simple star removal. It is recommended to use the Screen method when combining star and starless images back together.

Unscreen stars is enabled by default.

Star image stretching

When stars are removed before stretching then a different stretching can be used for the stars and potentially get better star colors.

Enhancements remove stars

Removes stars from the final image to generate a starless image and a separate stars image.

Enhancements Fix star cores

Sometimes star cores can be too bright and need to be fixed. There is an Enhancements option Fix star cores to fix star cores in the Enhancements / Generic enhancements section.

Reducing stars

Stars can be reduced either separately or when combining starless and star images.

Enhancements option Smaller stars reduce star sizes. Number of iterations can be selected when reducing star sizes. Value zero uses Erosion instead of Morphological Selection

When combining starless and star images it is possible to reduce stars using PixelMath expressions created by Bill Blanshan. There are three different methods: Transfer, Halo and Star. There is also a control parameter for each method. For details see this YouTube video

Combining starless and star images

For combining starless and star images there are three different options: add, screen and lighten. Add is a simple PixelMath operation to add stars back. Screen and lighten are equivalent to similar Photoshop blending modes.

Combine option can be set separately in the Postprocessing / Star stretching and removing section and Enhancements / Generic enhancements section.

Enhancements processing to starless and stars images

Enhancements processing has some additional options when combining starless and stars images.

To use the Enhancements option combine you need to have starless and stars images open on the desktop. Starless image must be selected as the target image.

With a default Auto mode AutoIntegrate tries to automatically find the stars image based on the starless image name. It assumes that your starless image name has a text starless and stars image name has a text stars. So a starless image sameprefix_starless_whatever is matched with a stars image sameprefix_stars_doesnotmatterwhatishere. You should get an error if a matching stars image is not found.

Using the select button it is possible to manually select the stars image from images open on the desktop.

Reduce stars options can be used to reduce star sizes during combine. These options use PixelMath equations created by Bill Blanshan.

AutoContinue

AutoContinue runs automatic processing from previously created LRGB/RGB, Narrowband or OSC/Color images. The idea with AutoContinue is that it is possible to start from integrated images and then run processing with different options. For example you can run AutoIntegrate with default settings and then use AutoContinue to run different options on the same images.

AutoContinue makes it possible also to run manually gradient correction or histogram transformation on the automatically processed images and then again continue automatic processing from there.

AutoContinue looks for images with fixed names and if it finds them it starts processing from those images. It is also possible to load integrated images for example from WBPP to the light files. If fixed image names are not found, option Integrated lights in Files tab is checked and light files have only one image for a filter then it is considered as an integrated light image.

Image names are based on intermediate images created by the script but they have a special extension to separate them from automatically generated images. An exception is integrated channel or RGB images which can be used as a starting point to run different processing on automatically created images. They are useful for example for testing with different stretching options or narrowband palettes.

Starting points and the search order for AutoContinue are listed below

1. AutoLRGB or AutoRGB - Final image for enhancements
2. L_HT + RGB_HT - Manually stretched L and RGB images.
3. RGB_HT - Manually stretched RGB image
4. Integration_filter_GCext - Gradient corrected integrated channel images
5. Integration_RGB_GCext - Gradient corrected integrated color RGB image
6. Integration_filter - Integrated channel images
7. Integration_RGB - Integrated RGB image

Not all images must be present, for example the L image can be missing.

Explanation of terms used in image names:

• filter = Mono camera filter name, one of L, R, G, B, H, S or O.
• CGext = Gradient Corrected image, for example manual DBE or GraXpert is run on image. Postfix can be _GC, _ABE, _DBE or _GraXpert.

Simple example of using AutoContinue

In the simplest case you can use AutoContinue after basic processing is done and all icons are still on the desktop. In that case you can for example change the stretching setting or some other options, set a new Window Prefix to avoid overwriting previous processing and then run AutoContinue.

Example to run manual gradient correction

Below is an example of how to run manual gradient correction on images and then continue automatic processing from there. The example assumes that a base processing has been done with AutoIntegrate.

1. First close all images on the desktop. For a mono camera, load channel images Integration_filter to the desktop. For example Integration_L, Integration_R, Integration_G and Integration_B. For color camera, load Integration_RGB image.
2. If you want to avoid overwriting previously processed images set the Window prefix.
3. Crop images to remove all black borders. If you want to use automatic cropping you should use option Save cropped images during base processing. Then it is possible to use cropped images with _crop extension.
4. Run gradient correction on images. New images must have an extension like _DBE or _GraXpert. So gradient corrected Integration_R could be Integration_R_DBE, or Integration_RGB could be Integration_RGB_GraXpert.
5. Select processing options and click AutoContinue.

Example to run manual stretching

Below is an example of how to run manual stretching on images and then continue automatic processing from there. The example assumes that a base processing has been done with AutoIntegrate.

1. First close all images on the desktop. For mono camera, load images Integration_L_processed and Integration_RGB_processed to the desktop. For color camera, load Integration_RGB_processed image.
2. If you want to avoid overwriting previously processed images, set the Window prefix.
3. Run stretching on images and rename images to L_HT and RGB_HT.
4. Select processing options and click AutoContinue.

External tools

These tools are not part of PixInsight and must be installed separately. AutoIntegrate uses them automatically when they are selected.

RC Astro tools support

In the Settings / Tools section there are options to use RC Astro tools. In the Tools tab there are some configuration settings for RC Astro tools which are described below.

BlurXTerminator

In the Tools / StarXTerminator, BlurXTerminator, NoiseXTerminator section there are settings for BlurXTerminator.

NoiseXTerminator

In the Postprocessing / Noise reduction section there are settings for NoiseXTerminator.

StarXTerminator

In the Tools / StarXTerminator, BlurXTerminator, NoiseXTerminator section there are settings for StarXTerminator.

There it is possible to select other than the default AI model. You can see the default AI model in the tooltip. AI models are stored in the PixInsight installation directory and have a .pb extension. At least in Windows they are in the PixInsight/library directory.

If you are getting tiling artifacts you can check Large overlap option. It runs slower but should give better results.

GraXpert support

It is possible to use GraXpert for gradient correction, denoising or deconvolution of the image.

GraXpert gradient correction

For gradient correction GraXpert can be used instead of default gradient correction. GraXpert can be used by checking the GraXpert option in the Settings / Tools section. Smoothing and correction settings can be configured. When GraXpert is used from the script it always uses the AI model when removing gradients.

By default no gradient correction is done. To use GraXpert for gradient correction you need to also check one of the gradient correction options in the Settings / Image processing parameters section.

GraXpert denoise

For denoising GraXpert can be used instead of default noise reduction. GraXpert can be used by checking the GraXpert denoise option in the Settings / Tools> section. Smoothing and batch size settings can be configured in the Postprocessing / Noise reduction section.

GraXpert deconvolution

GraXpert deconvolution can be used to improve start quality and sharpen the image. It is used is used instead of the default image sharpening. GraXpert deconvolution can be used by checking the GraXpert deconvolution option in the Settings / Tools> section. Strength and FWHM settings can be configured in the Tools / GraXpert section for both stars and objects (non-stars).

Option Use median FWHM uses median FWHM from the subframe selector as the FWHM value. This value is calculated during AutoIntegrate processing and saved to the image metadata. Value is also printed to the AutoIntegrate.log file with a name AutoIntegrateMEDFWHM.

GraXpert generic settings

GraXpert is run as an external process so it must be installed on the computer before it can be used.

To use GraXpert first the path to the GraXpert binary must be set in the Tools / GraXpert section.

GraXpert AI model must be loaded manually before it can be used from AutoIntegrate. To load the AI model, run GraXpert manually once and close it. AutoIntegrate uses the default model.

Note that AutoIntegrate supports GraXpert version 2.2.0 or later.

StarNet and DeepSNR support

StarNet2 can be used to remove stars from the image. Before StarNet2 can be used it must be installed to PixInsight.

DeepSNR can be used to reduce noise in the image. Before DeepSNR can be used it must be installed to PixInsight. In Postprocessing / Noise reduction section there are settings for DeepSNR.