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The final | The final step 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 | * 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. | 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 <math>N</math> is the number of images. | ||
* Average Stacking With Rejection | * Average Stacking With Rejection | ||
** Percentile Clipping: this is a one step rejection algorithm ideal for small sets of data (up to 6 images). | |||
** 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>). | ** 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. | ** 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>. | ** 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. | ** 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. | ||
Revision as of 07:34, 12 November 2014
The final step 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 [math]\displaystyle{ N }[/math] is the number of images.
- Average Stacking With Rejection
- Percentile Clipping: this is a one step rejection algorithm ideal for small sets of data (up to 6 images).
- 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.