Hi all

In the continuing trend of collaborative processing tips and techniques, open sharing of information and ideas, I've uploaded some raw data from my my excellent Jupiter session on the 11th May.

Last time, I provided the raw stacked images which had some slight noise reduction already applied. This time, I've provided the raw stacked image - absolutely no wavelet processing, no noise reduction or any other processing.

For those that want an extra challenge, I've also uploaded zipped up files of the raw frames of each channel for you to process completely in Registax yourself (including align/optimise/stack) and produce the final image.

The raw stacked images can be downloaded here. Each file is a ~1meg TIF file. Right-click on the link and click "save target as" to save the file. Note: Your browser might try and rename the file to include a ".TIFF" extension instead of ".TIF". Make sure you rename it otherwise registax may have trouble opening the file.

- Stacked Red Channel
- Stacked Green Channel
- Stacked Blue Channel

As a suggestion for the less initiated, open each channel one at a time in Registax, and it will take you straight to the wavelets page where you can start the processing. They are saved from Registax v4 - you should be able to open them in Registax without any problem. If you have problems opening them in Registax, ensure they are saved as a ".TIF" extension.
To create the RGB colour image, you may need to convert them to greyscale first depending on your normal processing routine and the program(s) you use.

If you want the extra challenge of processing the raw frame data in Registax yourself, you can download the (large) files here. Each file is approx 180 meg zip file. Right-click on the link and click "save target as" to save the file. You will need to extract the zip (it will create a subdirectory) and then drag the bitmap files into Registax.

- Red Channel Raw Frames (180meg zip file)
- Green Channel Raw Frames (180meg zip file)
- Blue Channel Raw Frames (180meg zip file)

The raw frames were captured at 30fps (1/30s exposure) for 40 seconds each channel. The raw frames have been preprocessed using ppmcentre/ninox, which included ranking the frames (lower numbers are better quality) and cropping. No other processing has been done on the raw frames. I have zipped up the best 900 of the 1200 captured frames, just to make the zip file a little smaller.

As a suggestion for the less initiated, I use an alignment box of 128px and align on 2-4 points. My final stack usually includes 600 frames in very good seeing - obviously much less (half that or less) when the seeing is worse.

An (un-inforced) requirement is that you post your processing steps when you post your version of the image. This will help everyone (myself included) learn from your method of processing and be able to compare the results.

The aim of this project is to:
a) Enable others to learn from people's processing methods and steps
b) Up-skill the planetary imaging community so that everyone can produce better planetary images
c) Show how much of an art form image processing can be, and how personal taste plays a large factor in the final result
d) Show what raw data looks like when captured with the right techniques, using the right equipment in very good seeing.

This data was captured on the 11th May 2007 from my home on the Central Coast, NSW Australia with my 12" newt on an EQ6, using a DMK21AF04 monochrome webcam with a 5x powermate and Astronomik RGB filters. Jupiter was at an altitude of 61°. Seeing for the session was rated at a variable 6.5-8.5/10, with this avi data captured during the high end of that range.

Have fun, and I look forward to seeing the results!

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Mike
. mikesalway.com.au - Astronomy and Photography by Mike Salway
. IceInSpace - The Australian Amateur Astronomy Community
. My Bio | My Jupiter 2007 Gallery | My Image Gallery

Well, it looks like I'm the first one out of the gate here.

I have processed the TIF files, and have not gone back to the source video streams.

As in my other attempts, I have relied almost exclusively on PixInsight (currently available in a time-limited full version for free). It's a tool with a fairly high learning curve, but it allows very fine control of the image. Most of the processing below was done in a 64-bit space. While the original data doesn't have that granularity, processing with this precision eliminates any rounding errors. Hey, it might not make any difference in this case, but it is cool knowing it can be done.

Here was my process:

1. Open each image in PixInsight, change to grayscale, save as FITs files
2. Open in CCDStack, align based on central region, save again
3. Color combine in PixInsight, using LRGB combine (w/o L) and an even 1/1/1 R/G/B balance
4. Apply a curves transform, generally darkening the image
5. Perform a Weiner deconvolution, 2.75 st. dev, 1.8 shape. This brings out the features in the planet's clouds.
6. Another modest curves to adjust contrast and a curve on color saturation (a PixInsight feature) to boost the color.
7. Another modest Weiner deconvolution, 1.5 st. dev and 1.75 shape
8. A very modest GREYCstoration noise reduction (0.2 magnitude)

This is really great data to work with. Mike, thanks for the opportunity! I've got to get one of those DMK cameras, although I'm waiting now as it is June gloom here in Los Angeles. Only a rare peek at the sky here near the ocean.

Clear skies,

--Andy

Attached Images

Jupiter-2-RGB-01v01.jpg (36.7 KB, 30 views)

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Observatorio de la Ballona

Nice work Andy, thanks for having a play.

__________________
Mike
. mikesalway.com.au - Astronomy and Photography by Mike Salway
. IceInSpace - The Australian Amateur Astronomy Community
. My Bio | My Jupiter 2007 Gallery | My Image Gallery

Thanks Mike, I'll take a stab at the original AVIs, but that depends on how the brush clearing goes this weekend.

I am surprised there weren't more takers on this project.

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Observatorio de la Ballona