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Messages - vicent_peris

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1
New Scripts and Modules / Re: Pattern subtraction scripts
« on: 2019 November 13 08:10:18 »
Hi,

Thank you so much for adding the GUI!

I'm currently writing an article for the website describing the scripts. I also made some improvements to the code and corrected some bugs. With a bit of luck it won't affect too much the GUI related code.

I'll upload my new code as soon as posible.


Thank you again.
Best regards,
Vicent.

2
Hi Lars,

The workflow is as follows:

- Acquire the bias and dark frames at the right gain for those lights.

- Configure BatchPreprocessing as follows:
   - Load the individual bias an dark frames for these lights.
   - Load the master flat you created in the previous with different gain. Don't worry since this master flat is already calibrated and only contains the proportion of light response per pixel.
   - Check the "use master flat" option. Keep unchecked "use master bias" and "use master dark" options.
   - As registration reference, select the same reference as you selected in the previous run.

- Run BPP in a different output directory. Don't activate to apply the light integration.

- Manually integrate all the lights for each filter. You can mix images with different gains, ImageIntegration will weight each frame according to its noise level.



Good luck,
Vicent.

3
Hi,

You can control the processor cores from inside PixInsight. This is done with the parallel command. Issue this command in PixInsight's console: parallel -p=4. This will limit the use of the processor to 4 cores.


V.



4
General / Re: ImageIntegration Reference Frame Anomaly
« on: 2019 July 11 08:20:25 »
Juan,

   I am glad you found something here.

   One more quick question comes to mind. Should it be considered best practice to do CosmeticCorrection on the calibrated integrated Flat Frame before using it?

   Since flats are usually reasonably bight anyway, they are much more likely to contain saturated pixels in the same location for all sets of the raw flats. It seems to me that calibration of the flats with equal duration short darks may not always work to help fully saturated pixels if their problem results from higher than normal sensitivity. Should we then run CosmeticCorrection (with CFA Awareness when using color cameras) just in case?


John

Hi John,

If you have a saturated pixel in the flat frame, it means that pixel has probably 30-40k ADUs in a few seconds. That pixel will be completely saturated in a 5-minute light exposure. That means that you won't be able to recover in any way the light signal in that pixel. So, in that case, it's not worth to apply CosmeticCorrection to the master flat; simply apply it to the light frames and, of course, use dithering in your imaging session.

Very important as well: always calibrate your flat frames with bias and dark to remove those additive components. Ideally, any signal in the master flat should have strictly a multiplicative component, since the master flat should only show the relative light sensitivity of each pixel in the image.


Regards,
Vicent.

5
General / Re: Fixed pattern noise?
« on: 2019 May 08 04:41:29 »
Hi John,

I tried the script in your image and it does not work. The only reason I find is that the lines are not completely vertical. So, IMO, the fourier solution is the best one.


Best regards,
Vicent.

6
New Scripts and Modules / Re: Pattern subtraction scripts
« on: 2019 May 08 04:40:11 »
Dear Vicent,

Thank you for your scripting.
Please allways inform about new work from you , our facebookgroup Pixinsight Ă–sterreich (you are member)  and  PixInformatik.

I forwarded your work allready.

Franz

Franz, thank you very much for forwarding the message from one board to the another. Next time I'll post in both.

Best regards,
Vicent.

7
New Scripts and Modules / Re: Pattern subtraction scripts
« on: 2019 May 08 04:38:59 »
Hi Vicent!
That sounds great!
Question: Will the LinearDefectDetection script be usable with ImageContainer?
So i can remove one column defect on all images of a filter session.

Gerald

Hi Gerald,

LinearDefectDetection does not work in batch mode. I designed that script with the idea in mind of having a high quality reference image. I use an integrated image without alignment. This improves a lot the defect detection.

The problem is that those defects come out in the final master because most of them were below the noise floor in the single subframes. So, asuming you have the same defects in all the image set, you can use that integrated image as reference to detect the defects; then, you apply LinearPatternSubtraction in batch mode with the previously generated defect table.


Best regards,
Vicent.

8
New Scripts and Modules / Re: Pattern subtraction scripts
« on: 2019 May 08 04:34:32 »
Hi,

I made multiple improvements to the code, just download again the new version in the first message.


V.

9
General / Re: Fixed pattern noise?
« on: 2019 May 06 05:31:14 »
Could you please upload the image? It's a good test.

Thanks,
Vicent.

10
General / Re: Fixed pattern noise?
« on: 2019 May 06 05:09:29 »
Hi,

If you want to apply that pattern to the target image, write this formula in PixelMath: $T - line_pattern + med( line_pattern ) .

If forgot to say that these scripts should be applied to the linear image; any delinearization will deliver non-optimal results.

In your image, you don't need to detect any defective line since you want to simply remove the banding over the entire image, which takes always entire columns.

Best regards,
Vicent.

11
Hi,

I just released some scripts to fix this kind of problems. I hope they will work with your images:

https://pixinsight.com/forum/index.php?topic=13595.0


Best regards,
Vicent.

12
General / Re: Fixed pattern noise?
« on: 2019 May 06 04:08:49 »
Hi,

Thanks, I just left a message in its corresponding board. Hope it will work for you!

https://pixinsight.com/forum/index.php?topic=13595.0


Best regards,
Vicent.

13
New Scripts and Modules / Pattern subtraction scripts
« on: 2019 May 06 04:03:40 »
Hi all,

I'm working on a new image, but the CCD camera is getting very old, so it has a sensor plenty of defects. To remove these defects I had to write some scripts. I'll be writing an online article about the scripts in early June. The scripts don't have a GUI at this moment (and I have no plan to add it, I simply don't have the time). So anybody is welcome to code it. :-)

The attached image shows an integration of all the H-alpha exposure without alignment. This image increases the signal to noise ratio of all the defects in the image, which are the following:

- The top and bottom dark bands are some sort of gradients introduced (I guess) by the amplifier. These gradients vary in intensity from frame to frame.

- The diagonal dark structure at right bottom is an RBI residual coming from the flats on the afternoon. This planetary nebula has an extremely low surface brightness, and shooting with RBI supression would increase the background noise, so always do a regular readout. The problem is that you can have an RBI residual of varying intensity in certain frames.

- The CCD has a lot of entire and partial defective columns. More of 10% of the columns are now defective, which means that you cannot use CosmeticCorrection because you would be painting a lot of data...

These are the scripts I've developed:

PatternSubtraction script:

Script to subtract a fixed pattern from an image. This script can correct the active image or a list of images in an specified input directory.

To correct the RBI residual signal, I made a master dark with RBI-cancellation readout. Then, you remove the small-scale components with MMT. This image will work as the pattern reference. The script can be applied to the active image or a list of images in an input directory. In any case, you need to open the processed pattern reference image and specify its identifier in the script setup. The script works by progressively subtracting the pattern from the image until it finds a minimum in the noise measurement. I'm also attaching the RBI pattern reference image.

LinearDefectDetection script:

Script to detect defective columns or rows in a reference image. It can detect entire or partial defective columns or rows. The best approach to detect these line defects is to use an integrated image of non-aligned images. This way, we increase the signal to noise ratio of the lines, which is crucial to be able to detect them above the noise floor. This wouldn't be possible in a single subframe because most of the defects are well below the noise of the image and they only appear as diffuse lines in the integrated image. So, first you need to generate this kind of master image. The script will be always applied to the active image. It takes its time to finish since the algorithms are complex, but the time is well worth and it's a one-time process.

PartialLineDetection script:

A stand-alone version of the partial line detection function. It's always applied to the active image, which should be an integrated image of the subframes without alignment, in the same way as in the LinearDefectDetection script. It writes a CosmeticCorrection-compatible defect table as well.

LinearPatternSubtraction script:

Script to correct residual column or row patterns in an image. It can read the previously generated defect table and correct the defects on the active image or in an image list. It is possible to correct all the entire columns or rows in an image and, at the same time, correct the specified defects in the defect table.



The script writes a CosmeticCorrection-compatible defect table to disk. You can then use this table in CosmeticCorrection, but it better (IMHO) if you use this table to correct the defects with LinearPatternSubtraction.

All these scripts are configured by modifying the properties of the Config function. If you want to give it a quick try, open the LinearPatternSubtraction script in the script editor. Set the correctEntireImage property to true, don't specify a defect list in the partialDefectsFilePath property, and modify the backgroundReferenceLeft, backgroundReferenceRight, backgroundReferenceTop and backgroundReferenceBottom properties to select the right area in your image. Then, hit F9 to apply the script to the active image. It will correct and vertical banding in your image.

The scripts I'm sharing solve all the problems above. Feel free to contribute. I guess my code won't be very smart since this is my first time programming in an object-oriented language (my only experience up to now was shell scripting). But the code is very well commented and documented. PLEASE READ THE INCLUDED DOCUMENTATION!

Feel free to ask anything about the scripts and how to set them up.


Thanks,
Vicent.

14
General / Re: Fixed pattern noise?
« on: 2019 May 05 13:27:10 »
Hi,

CanonBandingReduction can do a good job on this image. But I just wrote a script that will completely eliminate this banding. I will release the script in the next weeks.


Best regards,
Vicent.

15
New Scripts and Modules / Re: Subframe Selector PCL Module
« on: 2017 November 17 03:31:26 »
Hi,

Congratulations for your new module. I want to add a couple ideas:

- I also think the cumulative probability graph can be very useful to establish a threshold.

- Since this is a PCL module, I think it would be really useful if your tool is able to monitor a directory to analyze newly saved images. To would be open the possibility to monitor in real time the image quality while acquiring data (inside or outside PixInsight).


Best regards,
Vicent.

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