Image Acquisitionand Proces

110 Image Acquisition Processing with LabVIEW
5.5 USER SOLUTION: IMAGE AVERAGING WITH
LABVIEW
Don J. Roth
Senior Research Engineer
NASA Glenn Research Center
Cleveland, Ohio
e-mail: don.j.roth@grc.nasa.gov
Many data acquisition processes are besieged by noise, including those that are
image-based. 1 Signal averaging is a simple and popular technique to reduce noise
or increase the signal-to-noise ratio (SNR). SNR is proportional to the square root
of the number of averages. Based on the circular buffer technique, the routines I
present allow the running image average of the most recent x number of images,
with the number being continuously adjustable in real-time, without disruption or
discontinuity of the averaging computation. The advantage of this is that you can
manage the trade-off between noise reduction and the amount of processing in the
time-domain during the test, if so desired. In other words, you can tune to, or “dial
in,” the level of SNR desired while the experiment is running and actually see the
in-situ effect on the image (Figure 5.37).
When dealing with continuous single shot measurements, a one-dimensional
array is needed to hold the collection of point measurements. When dealing with
waveforms, a two-dimensional array is needed to hold the collection of one-dimensional
waveforms. When dealing with images, a three-dimensional array is needed
to hold the two-dimensional image frames. LabVIEW functions (such as initialize
array, array subset and insert into array) need to be expanded to deal
with the different array sizes in each of the averaging schemes, as shown below.
This is achieved by right clicking on the appropriate input terminal of the function
and selecting “add dimension” (Figure 5.38).
Image averaging was performed by indexing out the columns of the threedimensional
array and then using the LabVIEW Mean function (from the Mathematics,
Probability and Statistics subfunction palette) on each column (one-dimensional
array) within the three-dimensional array. To better visualize this, consider
each image frame as a checkerboard of squares (pixels). Stack the checkerboards
on top of each other. Now extract out the vertical columns at every location and
calculate the mean of each one. The result is a two-dimensional array of mean values
(or an image average) (Figure 5.39).
The National Instruments Developer Zone shows an example running image
averager using IMAQ Vision functions 2 that operates somewhat differently than the
one presented here. In the National Instruments example, after a speciÞed number
of images are averaged, the averaged image becomes the Þrst image for the new set
of images to be averaged, whereas the routines presented here allow the running
image average of the most recent x number of images.
1
Roth, Don J., LabVIEW Technical Resource, Vol. 8, No. 4, pp. 12–14.
2
NI Developer Zone (http://zone.ni.com/) Example: Real-time Image Averaging.vi.