1.
Restoration of noisy Scanning Tunneling Microscope images
G.M.P. van Kempenl, P.M.L.O. Scholte2, I.T. Young’, F. Tuinstra2
1Pattern Recognition Group, Trystallography Group, Faculty of Applied Physics, Delft
University of Technology, P.O. Box 5046,2600 GA Delft, The Netherlands
Abstract the difference between the restored image j ( x , y ) and
We have compared and improved several the true image f(x,y). The optimal linear restoration
implementations of the Wiener filter to remove noise filter, given g(x,y), is then known as the Wiener
effects from Scanning Tunneling Microscope images. smoothingfilter [3] , which is given by
We have found that the implementation of Weisman
et al. [61, using the noise model of Stoll et al. 121, F(u, V ) F * (U,v)
provides the best pelformance on both simulated and w(u, v) =
F(u, V ) F * (U,v )+ N(u, V ) N * (U,v)
real STM images.
1
1: Introduction 

4
1+ Snn (U’
Since its invention by Binnig and Rohrer in 1981 [l], Sf (44
the Scanning Tunneling Microscope (STM) has proved where F(u,v) and N(u,v) the Fourier transforms of
are
to be a very useful instrument for surface science. A respectively the true image and the noise. SfJ(u,v)
STM images the height of a surface on atomic scale, by denotes the power spectrum of the true image, S,,(u,v)
measuring the current of tunneling electrons between of the noise. The restored image is given by
the tip of the microscope and the surface, when a
voltage difference is applied between them. Variations (3)
in the tunneling current are imaged by the STh4 as
variations of the height of the surface. with ?(u,v) the Fourier transform of the restored
Unfortunately, images made with a STh4 suffer from image and G(u,v) of the observed image. Since the
various distortions. We have found that the internal power spectrum of the true image and the noise are in
noise of the STM is the main source of distortion of general not known, one has to use an estimate or a
STM images made in air. This distortion is model of them. We have compared two models of the
characterised by long stripes in the scan direction, and noise power spectrum with real noise measurements,
has been characterised as socalled I/fnoise [2].This and tested five estimations of the true power spectrum
type of noise is strongly correlated and has a selfsimilar on simulated images.
frequency distribution. The removal of this internal Z  g From experiments [ 2 ] , Stoll and Marti have found
like noise from STM images is the main goal of the that the power spectrum of STh4 noise is determined by
research presented here. IJlike noise at low frequencies. They believe that this
noise is caused by very slow unintentional motion of the
2: Image acquisition models applied to STM tip perpendicular to the scan direction and by the
electronic components of the STM’s feedback system.
To model the acquisition of a STM image, we have Based on these assumptions they model the noise power
assumed that the internal STM noise n(x,y) is additive spectrum as,
and noncorrelated with the “true” image f(x, y) of the
surface, (4)
g(x,y)=f(x,y)+n(x,y), (1) [ + (vl?L,)ilf
where g ( x , y ) is the observed STM image. We have
used the mean square error as a criterion to minimise
134
1051465U94 $04.00 0 1994 IEEE
2.
where L is the number of points per scan line. They
, Finally, we have tested a modified version of the
argue that the effect of Z/flike noise is much more Wiener filter proposed by Weisman et al. [6][5],
pronounced in the v than in the udirection, which is in
1
contradiction with the model of Park and Quate [4], who w(u, v) = 9 (7)
modelled the noise power spectrum as NUB.From our CL4
own noise measurements 151 we have found that the P
model of Stoll and Marti describes real STM noise more [U2 + (v/2Lx,’]~
accurately than the model of Park and Quate. 1+
To restore STM images, Stoll and Marti have used
the following implementation of the Wiener smoothing in which we have used the noise model of Stoll and
filter, Marti instead of the model of Park and Quate.
1
w(u,v) = 9
3: Simulations with the Wiener filter
CL4
1+ R
In our tests with simulated and real STM images, we
have mainly used graphite as a test surface. Mizes et
in which the true power spectrum is modelled with a1.[7] show that STM images of graphite are well
the constant lla. We have estimated this power approximated by a summation of three cosine waves
spectrum, by using equation [ 11, as the difference oriented at 120’ angles.
between the observed image and the noise model of Since a good physical model for the internal STM
Stoll and M r i This leads to the following formulation
at. noise is lacking, we have used a simple method to
of the Wiener smoothing filter [5], simulate I/flike noise. The I/’noise was generated by
c A
inverse Fourier transforming a function with a
amplitude and a uniformly distributed random phase
(fig. 111, right).
0 otherwise
wiener3 test1

 periods
o
4.0
12.0
20.0
28.0
+ 36.0
sim. graphite
+ 440
1 . 1 . 1 ’ I I
2.0 4.0 6.0 8.0 10.0
signal to noise ratio wiener3 (eg 6 )
Figure 1: The left picture shows the normalised mse between the true image and restored image of our Wiener
implementation (eq. 6) as function of the signal to noise ratio between the true image and the noise for different
numbers of the period of the graphite lattice, with a constant p of 1.50. The right image shows the filter
performance of our Wiener implementation,with the true image (upper left), the noise realisation (upper right),
the distorted image (down left) and the filter output (down right). The signal to noise ratio is 1.O, p 1.0 and the
number of lattice periods is 8.0. The normalised mse of the filtered image is 0.095.
135
3.
STM image wiener3 I e g . 6 1 wiener5 Ieg. 7 )
Figure 2: Wiener filtering of a real STM image. A STM image of graphite is shown, wiener3 denoted our
implementation, wiener5 the modified filter of Weisman et al. with the filter parameter a of 25.0.
We have tested five Wiener filter implementations: and have fit the amplitude of the noise on the spectra of
the ideal Wiener filter (eq. [2]), the ideal filter with the real STM images.
noise model of Stoll et Marti (to test this noise model), The results of the two Wiener filter implementations
the implementation of Stoll and Marti, our differ from what we would have expected from the
implementation and the modified Weisman filter. simulations. The modified filter of Weisman et al. has
The performance of these Wiener filters have been performed significantly better then our implementation
tested as function of the signaltonoise (SNR) ratio (fig. 2). We have found that this difference in
between the true image and the noise. The S N R ratio is performance can be explained by the deviations of the
determined by the noise amplitude A , the p of Z @ STMnoise from the Z/fnoise model of Stoll. These
noise, and the period P of the graphite image. We have deviations of real STMnoise are, apparently, larger
varied A , p and P so that they represent the range of than our simulated Z/fnoise.
these variables in real STM images.
The performance of the Wiener filters have been References
determined by calculating a normalised mean square
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power of the true image.
[2] E.P. Stoll and 0. Marti, “Restoration of scanning tunneling
From our tests [ 5 ] with the ideal Wiener filter, we microscope data blurred by limited resolution, and
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the simulated graphite images. The second filter gives pp.222229.
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[7] H.A. Mizes, Sangil Park and W.A. Harrison, “Multipletip
We have applied our Wiener filter and the modified interpretation of anomalous scanningtunnelingmicroscopy
Weisman implementation of the Wiener filter to real images of layered materials,” Physical Review B 36(8)
STM images. We have found that the stationary tip (1987) pp.44914494.
[8] M. Aguilar, E. Anguiano, A. Diaspro and M. Pancorbo,
images describe the noise in the real images “Digital filters to restore information from fast scanning
qualitatively well, but that they underestimate the total tunneling microscopy images,” Journal of Microscopy
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