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==Stacking== | ==Stacking== | ||
The final thing to do with Siril is to stack the images. Go to the "stacking" tab, indicate if you want to stack all images, only selected images or the best images regarding the value of FWHM previously computed. | The final thing to do with Siril is to stack the images. Go to the "stacking" tab, indicate if you want to stack all images, only selected images or the best images regarding the value of FWHM previously computed. Siril proposes several algorithms for stacking computation. | ||
* Sum Stacking | |||
This is the simplest algorithm: each pixel in the stack is summed using 32-bit precision, and the result is normalized to 16-bit. The increase in signal-to-noise ratio (SNR) is proportional to <math>\sqrt{N}</math>, where N is the number of images. | |||
* Average Stacking With Rejection | |||
** Sigma Clipping: this is an iterative algorithm which will reject pixels whose distance from median will be farthest than two given values in sigma units (<math>\sigma_{low}</math>, <math>\sigma_{high}</math>). | |||
** Median Sigma Clipping: this is the same algorithm except than the rejected pixels are replaced by the median value of the stack. | |||
** Winsorized Sigma Clipping: this is very similar to Sigma Clipping method but it uses an algorithm based on Huber's work <ref>Peter J. Huber and E. Ronchetti (2009), Robust Statistics, 2nd Ed., Wiley</ref> <ref name="Juan">Juan Conejero, ImageIntegration, Pixinsight Tutorial</ref>. | |||
** Linear Fit Clipping: this is an algorithm developed by Juan Conejero, main developer of PixInsight <ref name="Juan" />. It fits the best straight line (<math>y=ax+b</math>) of the pixel stack and rejects outliers. This algorithm performs very well with large stacks and images containing sky gradients with differing spatial distributions and orientations. | |||
These algorithms are very efficient to remove satellite/plane tracks. | These algorithms are very efficient to remove satellite/plane tracks. | ||
Revision as of 23:51, 27 October 2014
Siril processing tutorial
- Convert your images in the FITS format Siril uses (image import)
- Work on a sequence of converted images
- Pre-processing images
- Registration (PSF image alignment)
- → Stacking
Stacking
The final thing to do with Siril is to stack the images. Go to the "stacking" tab, indicate if you want to stack all images, only selected images or the best images regarding the value of FWHM previously computed. Siril proposes several algorithms for stacking computation.
- Sum Stacking
This is the simplest algorithm: each pixel in the stack is summed using 32-bit precision, and the result is normalized to 16-bit. The increase in signal-to-noise ratio (SNR) is proportional to [math]\displaystyle{ \sqrt{N} }[/math], where N is the number of images.
- Average Stacking With Rejection
- Sigma Clipping: this is an iterative algorithm which will reject pixels whose distance from median will be farthest than two given values in sigma units ([math]\displaystyle{ \sigma_{low} }[/math], [math]\displaystyle{ \sigma_{high} }[/math]).
- Median Sigma Clipping: this is the same algorithm except than the rejected pixels are replaced by the median value of the stack.
- Winsorized Sigma Clipping: this is very similar to Sigma Clipping method but it uses an algorithm based on Huber's work [1] [2].
- Linear Fit Clipping: this is an algorithm developed by Juan Conejero, main developer of PixInsight [2]. It fits the best straight line ([math]\displaystyle{ y=ax+b }[/math]) of the pixel stack and rejects outliers. This algorithm performs very well with large stacks and images containing sky gradients with differing spatial distributions and orientations.
These algorithms are very efficient to remove satellite/plane tracks.
After that, the result is saved in the file named below the buttons, and is displayed in the grey and colour windows. You can adjust levels if you want to see it better, or use the different display mode. In our example the file is the stack result of the 80% best files, i.e., 96 over 119 files.
The images above shows you the result displayed in Siril with the Histogram Equalization tool. Note the improvement of the signal-to-noise ratio regarding the result given for one frame in the previous step (take a look to the sigma value). Now should start the process of the image with crop, background extraction (to remove gradient), and some other processes to enhance your image. To see processes available in Siril please visit this page.
The images above picture the result in Siril using the Histogram Equalization rendering mode. Note the improvement of the signal-to-noise ratio regarding the result given for one frame in the previous step (take a look to the sigma value). The increase in SNR is of [math]\displaystyle{ 38/3.9 = 9.7 \approx \sqrt{119} = 10.9 }[/math]
End of the processing tutorial. Return to the main documentation page for more illustrated tutorials.
Here, comparison between the same crop of calibrated single frame and stacked result.
Now should start the process of the image with crop, background extraction (to remove gradient), and some other processes to enhance your image. To see processes available in Siril please visit this page.
Here an example of what you can get with Siril:
End of the processing tutorial. Return to the main documentation page for more illustrated tutorials.