Siril:Tutorial preprocessing/en

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 (Global star alignment)
• Stacking

Pre-processing images

Once a sequence is loaded, images can be pre-processed, registered and stacked. The pre-processing is an optional (but important) step and involves offset, dark and flat frames.

• BIAS (OFFSET)

A BIAS frame is an image taken with the shutter closed and the shortest possible exposure time. Basically it corresponds to an exposure of 1/4000s with modern DSLRs. The BIAS shows the electronic noise and readout signal of the camera and the more BIAS exposures are used for the calculation of the master BIAS, the less noise will be introduced into the corrected images. Note that BIAS frames MUST be taken at the same ISO than light frames. Also, you can stack bias frames with the median algorithm (or Winsorized by checking the rejection levels at the end of the process (< 0.500)), but be sure to use No Normalisation !

Here, a master BIAS, calculated from 10 single images with Winsorized sigma clipping.

12:58:34: Pixel rejection in channel #0: 0.368% - 0.354%
12:58:34: Integration of 10 images:
12:58:34: Normalization ............. None
12:58:34: Pixel rejection ........... Winsorized sigma clipping
12:58:34: Rejection parameters ...... low=3.200 high=3.300

• DARK

Dark frames are made at the same exposure time and ISO as the subject light frames but in the dark: use your lens/telescope cap for example. They should also be made at approximately the same temperature as the light frames, this is the reason why we make dark frames at the end, or in the middle of the imaging session. The dark frames are used to remove the dark signal from the light frames and like the BIAS frames, the more dark exposures are used for the calculation of the master dark, the less noise will be introduced into the corrected images. Also, you can stack dark frames with the median algorithm (or Winsorized by checking the rejection levels at the end of the process (< 0.500)), but be sure to use No Normalisation !

Here, a master dark, calculated from 10 single images with Winsorized sigma clipping.

13:00:40: Pixel rejection in channel #0: 0.340% - 0.384%
13:00:40: Integration of 10 images:
13:00:40: Normalization ............. None
13:00:40: Pixel rejection ........... Winsorized sigma clipping
13:00:40: Rejection parameters ...... low=3.200 high=3.300

• FLAT

Telescopes usually do not illuminate the detector homogeneously. Moreover, dust on optical surfaces and sensor lead to dark spots on resulting image. To correct these effects one need to divide each light images by the master flat which is the median of single exposure of a homogeneously illuminated area.

Siril automatically evaluates the normalisation coefficient used in the flat division. However, this value can be over-evaluated and in this case you will be warned into the console output. For example you can have this output :

Overflow detected, change level value in settings: 3278.00 is too high.

So, the solution consists to uncheck the auto-evaluation in settings and define your own value, smaller than 3278 in this example. Also, you can stack flat frames with the median algorithm (or Winsorized by checking the rejection levels at the end of the process (< 0.500)), and choose Multiplicative normalisation.

Here, a master flat, calculated from 9 single images with Winsorized sigma clipping method. Note that vignetting and dust are visible. Bayer pattern is also visible. Note that it is recommended to check the "Multiplicative normalisation" button in the stacking when creating the master flat.

13:05:23: Pixel rejection in channel #0: 0.388% - 0.304%
13:05:23: Integration of 9 images:
13:05:23: Normalization ............. multiplicative
13:05:23: Pixel rejection ........... Winsorized sigma clipping
13:05:23: Rejection parameters ...... low=3.400 high=3.700

Pre-processing

The preprocessing operation consists in computing the following equation:
calibrated image [math]\displaystyle{ = \frac{light - masterDark - masterBias}{masterFlat - masterFlatDark - masterBias} }[/math]

For DSLR images, when dark optimization is not used and masterFlatDark not needed, one good method is to process like that:

• Integrate all bias to build master-bias
• Integrate all darks to build master-dark
• Calibrate flat frames with master-bias
• Integrate all calibrated flat to build master-flat
• Preprocess light frame with master-dark and master-flat

If you want to do the whole processing batch, once again with no masterFlatDark but with dark optimization, you must remove the master-bias from the master-dark before:

• Integrate all bias to build master-bias
• Calibrate dark frames with master-bias
• Integrate all calibrated darks to build master-dark
• Calibrate flat frames with master-bias
• Integrate all calibrated flat to build master-flat
• Preprocess light frame with master-bias, master-dark and master-flat

The pre-processing does not overwrites original FITS files from the sequence but creates new FITS files with the pre-processed data. The new file names are the same than the original prefixed with pp_ if you leave the default value in the prefix box. Below is an example of pre-processing using offset and dark files. Make sure that loaded sequence is the sequence of your light frames. Also, note that at the end of the process, the new sequence is loaded.

Since 0.9.2 version, a new tool is available in the preprocessing tab: cosmetic correction. This tool search deviant pixels through master-dark and will correct them. You always should use this tool if you own master-dark. Please, pay attention to the CFA box. Check it if your monochrome pictures are CFA images. If you don't have master-dark (which should be avoided), you could use automatic cosmetic correction tool available through the "image processing" menu.

Results examples

• Result of pre-processing

The result of a single frame pre-processed is given below. Note how dust have been removed from the light frame.

Another example of a well executed pre-processing stage is illustrated in the next animation. Look how the pixels of the thermal signal are removed from the picture.

Special case of demosaicing

• Demosaicing (for Bayer-Pattern images only)

Last step in pre-processing images consist in getting back Red, Green and Blue channels using interpolation algorithms. To do that, Siril gives the choice between 4 different algorithms. - VNG (default) - Bilinear (low quality but fast) - AHD - Super Pixel (no interpolation, the pattern RGGB, or other, is used as a super pixel and the image size is divided by four). Make your choice in the window below by clicking on Settings and Debayer tab.

Make your choice in the window below by clicking on Settings and Other Debayer tab.

.

To process the demosaicing, go back to Conversion Tab and fill the field as the example below:

Then click on the Debayer Button which should be sensitive. The demosaicing process should start and will take some time following the method you used and the CPU you own.

Finally you will have a new set of pre-processed light frames, automatically loaded in Siril, that you could register. Please note that at this stage you always have a lot of noise in your image. Indeed, pre-processing images with Dark, Bias and Flat do not remove noise but unwanted signal. The following Picture shows you the result of demosaicing. It is displayed in Histogram Equalization mode in order to enhanced the visibility of signal but without altering your image.

Next item of the tutorial: Registration (manual image alignment).