Table of Contents
Advertisement
When the masters are saved to disk, they are tagged with FITS Header
Keywords to indicate whether these subtractions have been done. In that way
when they are loaded again later, MaxIm LT will know whether they need to
have these subtractions performed or not.
3.5 Calibrating and Combining Images
While calibration reduces or removes the majority of the problems caused by
bias, dark current, and light sensitivity variations in a camera sensor, there is
still random noise remaining in the actual scene that the sensor records. If a
pixel receives 100 photons of light from a target, there will be 10 photons of
quantum photon noise due to the statistical nature of light. Some additional
noise will be present due to camera read noise and dark current noise.
The key thing to note here is that this noise is random in nature and varies
from frame to frame, whereas the object of interest, such as a distant galaxy,
generally does not. By combining numerous individual frames of the same
subject, the subject's signal steadily increases in a linear fashion. However,
since the noise in a single image is random, its increase in a combined image
follows a Poisson statistical distribution and so it increases more slowly.
Combining images therefore increases the overall signal-to-noise ratio in the
final result, yielding both smoother subject and background appearances.
Combining files (also known as "stacking") is not limited to your light frames,
but is also applicable to your calibration frames, since all imaging frames
include noise.
When you add or subtract images, the noise is always additive. Subtracting a
single dark frame from a light frame will remove large pixel-to-pixel variations
in the average accumulation of dark current, but it will also increase the
random noise in the light frame by 41%. If instead you averaged sixteen dark
frames together prior to subtraction, the noise will only be increased by 10%.
The standard combine method is to Average the frames. This produces the best
results for purely random Gaussian noise. Unfortunately if there is an "outlier"
pixel on one frame (e.g., a cosmic ray hit) then it will be included in the
average.
Median combine is much more effective at suppressing outlier pixels.
Unfortunately, median combining increases the noise level 25% compared to
averaging. When median combining flat-field frames, renormalization is also
required. This ensures that each frame is at the same average brightness.
MaxIm LT does this automatically.
An alternative to Median combine is to use Sigma Clipping or Standard
Deviation Masking. These techniques throw out outlier pixels and then average
the remaining. They are in effect a compromise between median and average,
combining the noise reduction advantages of Average with the outlier pixel
rejection of Median combine. You can also select renormalization options for
these methods.
SBIG STF SERIES - CAMERA USER'S MANUAL
34
Advertisement
Table of Contents
