1. CCDStack Version 2 Tutorial B. Waddington 4/19/2011
CCDStack Take 2
CCDStack Version 2 Tutorial
B. Waddington
4/19/2011

2. Stack-based Rejection
Compute statistics on each “column” of pixels in a stack of images…

3. Stack-based Rejection
Basic Concept
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” │

4. Stack-based Rejection
Individual pixels in each column are rejected as being outliers…

5. Rejected Pixels 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)

6. Data Rejection Choices – Sigma Clip
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 │

7. Sample Values in a “Pixel Stack”
Bogus values

8. Rejection by Sigma Clip3σ 2σ
3-Sigma
Clip

9. Rejection by Sigma Clip3σ 2σ
2-Sigma
Clip
2-Sigma

10. Poisson Data Reject
Uses a theoretical “spread” (sigma) assuming a Poisson distribution of data
Poisson sigma == Sqrt(average signal)
Average signal value calculation includes use of camera gain and read noise
Camera values need to be correct

11. Poisson Data Reject
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

12. Workflow Calibration Registration Normalization Data rejection
Combine (mean)
DDP processing for luminance
Sharpening for “preview”

13. Registration Tips Pick whatever image is well-framed as reference
CCDIS is preferred – requires CCDInspector plug-in ($90)
You can mix and match alignment methods as desired
Help file has good explanations
“Apply” alternatives
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

14. Normalization Pick your best quality image in the stack
Normalization is CRITICAL to data rejection
It can be repeated multiple times if needed

15. Data Rejection Tips 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

16. Color Combination Tips
Create an LRGB composite
Use the background adjustment option
Boost the contrast for better visualization
White adjustment options
Choose a known-white object
Select an entire spiral galaxy
Very good starting point on color balance

17. CCDStack Help Version 2 includes much-improved help
Context-sensitive help (F1)
Expanded information on various processing options
CCDWare support forums are monitored and used by program authors

18. References Streaming video tutorial:
Version 1 tutorial:
Original SIG presentation:
Where to get CCDStack: