CCDStack Take 2
CCDStack Version 2 Tutorial
B. Waddington
4/19/2011
Stack-based Rejection
Compute
statistics on
each “column”
of pixels in a
stack of
images…
Stack-based Rejection
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Basic Concept
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Estimate the “most likely value” for each column in the
stack – typically the “average” value
Look at the dispersion of values in that column relative to
the average
Reject those pixels that look “too far off the mark”
Rejection occurs for individual pixels in each
image, not the whole image
Most of the data rejection techniques are all about
defining “too far off the mark”
│
Stack-based Rejection
Individual pixels
in each column
are rejected as
being outliers…
Rejected Pixels
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What happens to the pixels that are
rejected?
They are assigned a “rejected value” and
are excluded from subsequent calculations
– but the original values are not lost
They may need to be given “imputed”
values if there are no “good” values to use
in their place (e.g. blooms)
Data Rejection Choices – Sigma Clip
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Computes the average and std. deviation of
the pixel stack; keeps only those pixels that
lie within a sigma factor of the average
Strong noise rejection but needs 10+
images for best results
Allows “tuning” based on the sigma factor
you specify
Can be iterated
│
Sample Values in a “Pixel Stack”
12
10
8
6
4
2
0
Pixel Value
or
e
M
25
50
25
00
24
50
24
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23
50
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Bogus
values
22
50
22
00
Frequency
Sampled Values of Single Pixel Position
Rejection by Sigma Clip
Frequency
Sampled Values of Single Pixel Position
3σ
2σ
12
10
8
6
4
2
0
3-Sigma
Clip
0
0
0
0
0
0
0
0
re
0
5
0
5
0
5
0
5
o
22
22
23
23
24
24
25
25
M
Pixel Value
Rejection by Sigma Clip
Frequency
Sampled Values of Single Pixel Position
3σ
2σ
12
10
8
6
4
2
0
2-Sigma
2-Sigma
Clip
0
0
0
0
0
0
0
0
re
0
5
0
5
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5
o
22
22
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24
24
25
25
M
Pixel Value
Poisson Data Reject
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Uses a theoretical “spread” (sigma)
assuming a Poisson distribution of data
Poisson sigma == Sqrt(average signal)
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Average signal value calculation includes use of
camera gain and read noise
Camera values need to be correct
Poisson Data Reject
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Can be used on small stacks – even two or
three images
Very strong noise rejection
May damage stars if the star sizes are not
pretty similar across frames
You need to try various sigma factors and
look closely at the results
Workflow
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Calibration
Registration
Normalization
Data rejection
Combine (mean)
DDP processing for luminance
Sharpening for “preview”
Registration Tips
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Pick whatever image is well-framed as reference
CCDIS is preferred – requires CCDInspector plugin ($90)
You can mix and match alignment methods as
desired
Help file has good explanations
“Apply” alternatives
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Nearest neighbor – image must be well-sampled,
alignment must be dead-on; be SURE you blink the stack
Quadratic B-Spline is a safer choice
Normalization
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Pick your best quality image in the stack
Normalization is CRITICAL to data rejection
It can be repeated multiple times if needed
Data Rejection Tips
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Use your best-quality image
Try for 1-2% data rejection on this
reference image by adjusting the “factor”
and looking at the “Info” window
STD Sigma Reject is a good all-purpose
choice
Poisson Reject is well-suited for small
stacks or nasty problems
Color Combination Tips
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Create an LRGB composite
Use the background adjustment option
Boost the contrast for better visualization
White adjustment options
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Choose a known-white object
Select an entire spiral galaxy
Very good starting point on color balance
CCDStack Help
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Version 2 includes much-improved help
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Context-sensitive help (F1)
Expanded information on various processing
options
CCDWare support forums are monitored
and used by program authors
References
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Streaming video tutorial:
http://www.ccdware.com/tutorials/ccdstack/overview.html
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Version 1 tutorial:
http://www.ccdware.com/Files/CCDStack%20Tutorial.pdf
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Original SIG presentation:
http://www.oc-aisig.org/docs/tutorials/
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Where to get CCDStack:
http://www.ccdware.com/