New DrizzleIntegration Tool Released

[Ignacio]

Many thanks, Juan. This makes perfect sense, as you preserve full resolution spatial information during registration, and then apply the geometric transformations to each color (bayered) matrix.

Question: can't there be information holes (by chance) that may require some level of interpolation/normalization at the end?

Ignacio

[Juan Conejero]

That depends on the number of images and the quality of dithering. You'll definitely need many dithered images. Bayer drizzle can be used with a drizzle scale of one as a non-interpolating deBayering method. It can be used also with drizzle scale > 1, but in this case a huge amount of data can be necessary to fill all the holes. With the necessary data sets, I think that both methods can yield very good results.

We currently don't have enough test data to draw conclusions in numbers terms. So all tests made by users will be very important. The new version of BPP with Bayer drizzle enabled should be ready in a couple days.

[Ignacio]

Thanks, Juan. Will try a first test (drizzle 1x) with my recent OmegaCent data, and compare with the standard drizzle 2x workflow.

Ignacio

[jerryyyyy]

Yes I see that, I assume the math projects the computations out into a large workspace.  To me seem a little like interpolation of data between existing pixels.

No, interpolation is a process of estimating new data points using existing ones. ...

Hi Ian,

Fantastic post and a very nice description of drizzle!

Yes, absolutely great explanation for someone at my level.  Just to kinda restate it in my language and in reference to my scope.  My 500mm FL Takahashi 180ED/SBIG STT8300M combo results in 2.21 arc"/pixel images.  This is undersampled in comparison to say my old 2000mm scope.  But, when my images are dithered the collected the images contain all the data that might have been picked up with the 2000mm scope.  This procedure wrings out the data that is actually in that undersampled data. 

I have to say I can see the difference when I run the new procedure.

One other question... how close to regular deconvolution does this procedure come... lazy me is always looking to avoid a laborious step...

[Carlos Milovic]

This has nothing to do with deconvolution
Although it would make things much easier, since the PSF will be better sampled, and data should correlate better with it.
On a side note, deconvolution and drizzle can be integrated into a single operation, and that is called super-resolution. We may implement this in the medium term.

[starhopper]

Hello,
I would like to post my first try with DrizzleIntegration.
My data is
a) undersampled (Canon EF200mm lens and KAF8300 Chip)
b) dithered
so I gave it a try and the result was a surprise. The attached file is a enlargement
of this image http://astrob.in/81586/C/ whitch is still without Drizzle at the moment. Left is without and right is with Drizzle.
Thank you for this tool.

[ajbarr]

Thomas can you explain what under sampling means?

Thanks

Albert

[Carlos Milovic]

Albert, undersampling is having less samples than what is needed to have an appropriate approximation to the underlying light distribution. In astronomy, this is related both to the optical resolution and the seeing. From the perspective of the sensor, roughly, you need 2 or 3 pixels to cover the FWHM of your stars. So, if you have very good seeing, or you are using a color filter array (as in the dslr cameras), you end with less samples than what is needed, and this is translated as a loss in sharpness.

[ajbarr]

Thanks Carlos. Does that basically mean if you have a lot of subs you don't need to use drizzle?

[Carlos Milovic]

It is not about the number of subs, but the spatial sampling. It is the size of the pixels and the coverage in the sky, compared to the seeing and scope's resolution.

This is closelly related to this:
http://en.wikipedia.org/wiki/Nyquist%E2%80%93Shannon_sampling_theorem

[starhopper]

Thomas can you explain what under sampling means? Thanks
Albert

Hello Albert,
Carlos provided the answer better than I ever can.
PS: undersampling is not good, but unavoidable when shooting with photo lenses. Now we have the drizzle tool 

Thomas

[MikeOates]

I have just tried drizzle on two sets of data and I have noticed that the rejection is not as good as I am getting a satellite trail remaining in the drizzle image in both cases, even though it is not in the image produced with the ImageIntegration tool. 'Generate drizzle data' was set and in the DrizzleIntegration tool 'Enable pixel rejection' was also set.

Mike

[whwang]

Bayer drizzle is pretty straightforward. One complication is the fact that CFA calibrated images have to be debayered prior to registration and pre-integration, but DrizzleIntegration has to have access to the CFA images. This can be solved by means of a little trick: replace the file names to which .drz files point to (which are debayered images) by the original CFA images. The other complication is that CFA images have to be split into separate RGB components in order to drizzle them, but we already have this: just load the CFA data as raw Bayer images. So the implementation looks like this:

1. Load a calibrated CFA image as a monochrome raw Bayer image.

2. Optionally, apply CosmeticCorrection.

3. Make a duplicate of the calibrated (and possibly cosmetized) CFA image and transform it to an RGB raw Bayer image (that is, split the RGB components as three channels). Save this RGB image in FITS format.

4. Apply Debayer to get an interpolated RGB image and save it in FITS format.

5. Apply StarAlignment to register the RGB image and save it. The generate drizzle data option of SA is enabled in this step.

6. Open the .drz file generated in step 5 and replace the file path (which points to the debayered image that StarAlignment has worked with) with the path to the RGB image that was saved in step 3. Save the modified .drz replacing the original data file.

7. Repeat steps 1-6 for all images in the batch task.

Now you can use ImageIntegration (with the registered images and the .drz files) and DrizzleIntegration (with the .drz files) in the usual way. The drizzle process will work with the original CFA data, not with interpolated data. This is the Bayer drizzle algorithm.

Hi Juan,

Let me try to understand this.  For R and B in a Bayer matrix, Bayer drizzle conceptually is just like normal 2x drizzle.  This is because out of every four pixels, only 1 is real R, or real B.  On the other hand, G is somewhat different.  Out of every 4 pixels, two are G, instead of one.  So to make Bayer drizzle works like a normal 2x drizzle for G, there must be two G channels, instead of one (in my naive thinking).  In the above 7 steps you wrote, which one has this G issue taken into account?

Cheers,
Wei-Hao

[Juan Conejero]

I have just tried drizzle on two sets of data and I have noticed that the rejection is not as good as I am getting a satellite trail remaining in the drizzle image in both cases, even though it is not in the image produced with the ImageIntegration tool.

Rejection is exactly the same on the ImageIntegration and DrizzleIntegration tools. They are identical by construction, so this "cannot fail" geometrically, modulo bugs.

However, if you are integrating few frames, the relative lack of SNR over rejected areas combined with some particular subpixel alignments may make rejected pixels visible as a result of different noise patterns or distributions after drizzle. This shouldn't happen if you integrate a sufficient number of images. How many frames are you using with drizzle?

[Juan Conejero]

Hi Wei-Hao,

Let me try to understand this.  For R and B in a Bayer matrix, Bayer drizzle conceptually is just like normal 2x drizzle.

Bayer drizzle is just the same drizzle algorithm applied to CFA data. You normally perform a 1x Bayer drizzle, not 2x, because filling the Bayer pattern holes requires a *lot* of images. Filling Bayer holes plus the extra grid spacing in a 2x Bayer drizzle requires a really huge amount of data. For the same reason, Bayer drizzle is usually applied without any drop shrink (drop shrink factor = 1).

A 1x Bayer drizzle process can be used as a nice deBayering algorithm that works with the actual raw data without any interpolation. In this case there is no resolution improvement, but provided that you have enough frames to cover the image, the results should be better than a normal deBayering with interpolation such as VNG, AHD, etc.

...out of every four pixels, only 1 is real R, or real B.  On the other hand, G is somewhat different.  Out of every 4 pixels, two are G, instead of one.  So to make Bayer drizzle works like a normal 2x drizzle for G, there must be two G channels, instead of one (in my naive thinking).  In the above 7 steps you wrote, which one has this G issue taken into account?

This is the idiosyncrasy of Bayer filters. There is two times more green data than red and blue in a Bayer CFA frame, and Bayer drizzle doesn't change this. Your final image will have roughly two times more signal in the green channel than on the red and blue channels. We could think on a variation of Bayer drizzle that attempts to compensate for this relative lack of signal, but this implies interpolation, which is just what we are trying to avoid with Bayer drizzle.