NEAIC2008

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NEAIC 2008 Narrowband Imaging - Great Results
from Poor Locales Friday, April 25, 2008 Neil
Fleming (www.flemingastrophotography.com)
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Agenda

• Light pollution? Why even try?
• Differences between regular RGB imaging and
  Narrowband imaging
• Capturing good data
• Initial processing workflow CCDStack
• Final processing workflow Photoshop
• My goal in this presentation is not strictly
  scientific, it is more the presentation of an
  enjoyable result
• PowerPoint, then on to the actual programs

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Light Pollution in Boston You Gotta Be Kidding!
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Imaging from LP Locales Why Even Try!?!?

• Typical raw and semi-processed result from
  Boston
• I waserdisappointed!

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Then I Found Narrowband Imaging
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What is RGB Imaging?

• RGB stands for Red, Green, and Blue
• This mix of primary colors is what our eyes use
  to interpret color
• All imaging starts with a black and white CCD or
  a one-shot color (OSC) CMOS sensor
• Monochrome cameras are used in conjunction with
  filters

• Your DSLR or OSC cameras utilize tiny
  red/green/blue (RGB) filters over the individual
  pixels, placed in a Bayer matrix pattern

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Whats the Difference?

• With either a black and white camera or a OSC
  camera, you use color filters to emulate the RGB
  results so you can obtain a color image
• The wavelengths captured are across the entire
  visible spectrum

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What Does RGB From Boston Look Like?

• Light pollution typically causes horrible
  gradients
• This is a sample of a stack of 3x8 minute
  exposures

• A little processing in Photoshop helps, but
  still, not great results
• Bright objects are better

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Narrowband Imaging

• Narrowband filters let through only the tiniest
  bandpass of light, light that is associated with
  emission lines in nebulae. Typically 3 to 13 nm
  in width
• Hydrogen-alpha (Ha) emission wavelength is 656.3
  nm
• Doubly oxidized oxygen (OIII) has its main
  emission line at 500.7 nm
• Sulfur (SII) emits at 672 nm

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More Results NGC6992 The Veil Nebula
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More Results IC1396 The Elephant Trunk Nebula
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More Results DWB-111 The Propeller Nebula
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Image Capture

• Go deep
• Go for a greater subexposure (sub) duration and
  overall exposure time than for RGB imaging
• My RGB subs would average 1-4 minutes in exposure
  time
• For narrowband, I shoot at least 30 minute subs
• Get lotsa data
• I aim for 18-30 hours of usable data for an
  object
• I strive for at least 12 subs per channel, more
  if possible
• This allows for a reasonable signal-to-noise
  ratio (SNR), and includes enough subs in the
  stack to do efficient data rejection of outliers
  (satellites, airplanes, and cosmic ray hits)
• Gather ample calibration frames
• 30 bias, 25-30 darks, 1,000,000 ADU of flats
  (30 for me)
• I get automated dawn flats

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Processing Workflow CCDStack

• Prepare calibration masters bias/darks/flats
• Load and calibrate your subs
• Apply DDP, and evaluate the quality of the subs
• Bloom rejection
• Registration
• Normalization
• Data rejection
• Channel master combines

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CCDStack Calibration Masters

• Dark Frames
• These are used to subtract out the effects of
  hot pixels
• Optimally, these are taken at the same duration
  and camera temperature as your light frames
• Flat Frames
• Used to accommodate light fall-off at the edges,
  as well as to eliminate dust motes
• Bias Frames
• Zero-duration dark frames used to time scale
  darks, and as a proxy dark frame for flats
• Prepare calibration masters bias/darks/flats
• I try to get 25-30 subs for each type of master
• Use some sort of sigma rejection or clip min/max,
  don't use mean
• This eliminates the impact of outliers like
  cosmic ray hits (all three types) and stars (for
  the flats)
• Bias-subtract your flats when you create your
  flat master to accommodate differing temperatures

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CCDStack Dark Frame Master Sample
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CCDStack Flat Frame Master Sample
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CCDStack Load and Calibrate Your Subs

• Load all of your subs into CCDStack
• Under Process, select Calibrate
• Select your appropriate dark, bias, and flat
  masters
• Apply to all

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Sample Uncalibrated Sub
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The Same Sub - Calibrated
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CCDStack Evaluate Sub Quality

• Rotate all of the subs to the same orientation
• Carefully evaluate your data!
• CCDInspector for contrast, aspect ratio, and FWHM
  evaluation
• Mark I eyeball as a second step, especially if
  your data is undersampled, for gradients and star
  aspect ratio
• Good / Marginal / Bad
• I discard the Bad subs, and keep the Good
  along with a few of the Marginal
• The larger the stack of good subs, the more of
  the marginal I can include

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The Good, the Bad, and the Ugly
Bad
Good
Marginal
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CCDStack Bloom Rejection

• Process, Data Reject, Procedures
• Select, Reject Blooms
• Set appropriate upper limit, e.g., 2000 ADU
• Apply to All
• Impute Rejected Pixels
• I use 0.2 pixels, with 3 iterations
• Apply to All

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CCDStack Bloom Rejection
Before
Rejected Bloom
Pixels Imputed
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CCDStack Image Registration

• I often image the same object over multiple
  nights
• This results in a little offset or a slight extra
  rotation between subs from each night
• Go under, Stack, Register

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CCDStack Image Registration

• Under the Star Snap tab, Select Reference
  Stars
• I pick 3 to 4 widely spaced, medium sized stars
• I then click on, Align All
• Blink through the stack to ensure that all subs
  are well-registered
• If not, I will reset, and first try a pass with
  two closely spaced bright stars, then do a second
  pass with the 3 to 4 widely spaced, medium sized
  stars (Dual-pass method)
• When aligned, move to the Apply tab and select
  a method for registration, like Quadratic
  B-Spline, and click, Apply to All

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CCDStack Image Registration
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CCDStack Image Registration
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CCDStack Image Registration
Unregistered
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CCDStack Image Registration
Registered
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CCDStack Image Normalization

• This is used to balance the individual subs
  contribution to the final combine
• The higher quality subs will contribute more,
  while the lower quality subs will contribute less
• Go to, Stack, Normalize, Auto, and click,
  OK

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CCDStack Data Rejection

• Data Rejection
• CCDStack allows you to reject poor data like
  satellite trails, cosmic ray hits, and airplane
  trails independently of the combine method!
• You do not have to rely on mean, median, etc., to
  get rid of these pests!
• Options include
• STD Sigma
• Poisson Sigma
• Each of these methods will throw out the
  outliers and average the remaining pixel values
• I often use the Poisson Sigma reject, with 1.6
  to 2 standard deviations (sigma multiplier)
• Larger stacks can take tighter tolerances

• Linear Factor
• Clip Min/Max

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Rejected Data a Good Subexposure
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Rejected Data a Lower Quality Subexposure
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CCDStack All Subs with Rejected Pixels
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CCDStack Channel Master Combine

• Data combine
• You can do any of the following
• Sum
• Mean
• Median
• Minimum
• Maximum
• I almost always use, Mean for complete data
  sets
• Ill use Sum if utilizing data sets in further
  combine steps

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CCDStack Mean Combine the Subs
Single Sub
Master Combine
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CCDStack Deconvolution

• I prefer the Positive Constraint algorithm
• Choose a medium star on a dark background, with
  25 iterations

Original
Deconvolved
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CCDStack The Ha Channel Master is All Done
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CCDStack Do the Other Channels

• Now do the OIII and the SII (if you have it) via
  similar processing steps
• After calibration and rotation, when I start
  registration, I re-load the Ha master and use
  that one to register the OIII and SII subs
• This allows for only one destructive
  registration step to be applied, not two!
• I then do a final tweak with DDP to maximize the
  presentation of the object
• You now have your Ha, OIII, and SII masters
• Color time!

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CCDStack Do the Other Channels
Ha
OIII
SII
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CCDStack Color Combine

• One approach is to do the initial color combine
  in CCDStack

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CCDStack Color Combine
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CCDStack Color Combine
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CCDStack Color Combine

• For narrowband work, unlike RGB, I prefer to
  "optimize" each channel in Photoshop (PS) before
  the color combine
• This helps to maximize the contribution from each
  data set, especially the OIII and SII
• So, at this point, I will save the FITS as a
  "scaled TIF" from CCDStack for each of the
  channel masters
• This squeezes or translates the umpteen million
  data values into the 16-bit color space

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Photoshop Color Combine

• I open each TIF in PS, and closely examine the
  histogram to make sure I have not clipped the
  data at either end
• I find the scaling process in CCDStack will
  usually clip just a bit on the dark end of the
  histogram
• So, I will go back into CCDStack, lower the dark
  value cutoff a bit, and re-save the scaled TIF
  until I am satisfied

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Photoshop Channel Optimization

• To "optimize" each channel in Photoshop (PS)
  before the color combine, I generally do
• A contrast curve adjustment layer
• Noise reduction layer
• Local contrast enhancement layer, created with
  Noel Carbonis Photoshop actions
• Sometimes a Shadow/Highlight adjustment layer
  (in PS-CS2 or higher)

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Photoshop Contrast Curve
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Photoshop Noise Reduction

• Do your favorite noise reduction process step
• Noise reduction in PS
• NeatImage
• Noise Ninja, etc.

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Photoshop Local Contrast Enhancement

• Local Contrast Enhancement with Noel Carbonis
  Photoshop Actions
• This works to increase the contrast in the
  mid-range of the image

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Photoshop OIII and SII

• Same process for OIII and SII

OIII
SII
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Photoshop Channel Combine
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Photoshop Channel Combine
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CCDStack vs. Photoshop Channel Combines

• Color combine early in CCDStack vs. later in PS

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Photoshop Color Enhancements

• Perhaps a bit of a Channel Mixer adjustment
  layer (I may add a bit of the Ha to the blue
  channel, to reflect the H-beta component)

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Photoshop Color Enhancements

• A Selective Color adjustment layer in this
  case, to reduce the overall yellow cast in
  certain colors and to reduce the magenta stars

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Photoshop Color Enhancements

• Curves adjustment layers to make the reds
  redder and the blues bluer
• I sometimes work by selecting a color range to
  pick up the desired reds or blues

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Photoshop Color Enhancements

• Socolor adjustments before and after

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Photoshop Remove the Magenta in the Stars

• I select the star cores, and work outwards by
  expanding and feathering the selection
• I then apply a Hue/Saturation adjustment layer
  to reduce the magenta cast

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Photoshop Sharpen to Taste

• PS sharpening, NIK Sharpener Pro, others

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Works Better on Undersampled Data
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Photoshop Crop for PresentationVoila!
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Dumbledore Version
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Summary

• Get lots of data
• Shoot at least 30 minute subs
• Aim for at least 12 subs per channel
• Gather ample calibration frames
• 30 bias, 25-30 darks, 1,000,000 ADU of flats
• Use some sort of sigma rejection or clip min/max,
  don't use mean
• Carefully evaluate your data, reject the bad
• Apply advanced data rejection techniques
  independently of the combine step
• Poisson Sigma rejection, Clip Min/Max

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Summary (continued)

• Use DDP and Deconvolution
• For subsequent data sets, always register the
  individual subs to the initial master that you
  produced
• Save your Channel Masters as individual black and
  white scaled TIFs
• Ensure that you do not clip the data as you save
  your masters as scaled TIFs and import into
  Photoshop

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Summary (continued)

• Channel Master Optimization
• Try a curves contrast layer
• Noise reduction in your favorite tool
• For mid-tone contrast enhancements, I like the
  Local Contrast Enhancement action from Noel
  Carboni
• For handling extremes in the image, I like the
  Shadow/Highlight adjustment in PS
• Do your color combine in Photoshop, after you
  have optimized the presentation of the individual
  channel data

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Summary (continued)

• Color adjustments in adjustment layers
• Channel Mixer
• Selective Color
• Curves (in concert with masks) to bring up the
  reds and blues
• Reduce the magenta in the stars by selecting the
  stars and working out, in combination with a
  Hue/Saturation adjustment layer
• Careful sharpening via multiple techniques
• PS sharpening, High Pass filtering, NIK Sharpener
  Pro, Focus Magic (additional deconvolution)

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(No Transcript)
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Questions? Narrowband Imaging Great Results
from Poor Locales Friday, April 25, 2008 Neil
Fleming (www.flemingastrophotography.com)