Diffraction SBIG STC-7, Руководство пользователя

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SBIG STC-7 CAMERA
USER’S MANUAL
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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 generally does not. By
combining numerous individua
l 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.
General workflow for calibrating and combining images
The following is a general summary of the basic workflow used in MaxIm LT to
calibrate and combine images. As such, consider it an overview. Numerous
parameters not cited here are available to tailor the process to specific imaging
types and requirements. Refer to the MaxIm DL Help file for full tutorials,
further information, and more detailed procedures, as required.