Pozo,
fold is
Jp» FE-
of the
rately.
inifold
In par-
he im-
1struc-
image
sity of
eed to
ity ex-
S way,
e con-
image
ng the
s done
nenta-
g. For
| com-
nages.
rocess
ortant
which
of the
of the
struc-
| gra-
. The
retain
ppen,
' sub-
ound-
rithm
othe-
of the
n de-
econ-
ction
used
is the
othed
yrma-
od at
thus,
| het-
cally
s like
omo-
rs of
ssing
able.
il 2004
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004
Figure 4: Details of the reconstructed images and their
graylevel values' distribution. From top to bottom: de-
tails of the original Spot image, details of the FRI and the
RMI, respectively reconstructed with the fields v4; and vo.
The fidelity to the original image can be adjusted, by grad-
ually incorporating as many details of the original image as
desired, so that the degree of smoothing can be controlled.
For a deeper validation of the method, we should perform
the algorithm on very high resolution images.
REFERENCES
Daubechies, I., 1992. Ten lectures on wavelets. CBMS-
NSF Series in Appl. Math., Capital City Press, Philadel-
phia (USA).
Frisch, U., 1995. Turbulence. The Legacy of Kolmogorov.
Cambridge University Press, Cambridge MA.
Grazzini, J., Turiel, A. and Yahia, H., 2002. Entropy esti-
mation and multiscale processing in meteorological satel-
lite images. In: Proc. of ICPR, Vol. 3, pp. 764-768.
Laporterie-Djean, F., Lopez-Ornelas, E. and Flouzat, G.,
2003. Pre-segmentation of high-resolution images thanks
to the morphological pyramid. In: Proc. of IGARSS,
Vol. 3, pp. 2042-2044.
Marr, D., 1982. Vision. W.H. Freeman and Co., New York.
1129
Le ra 1 RE iur
SR x eut nA : 1
Use à
EE NUS WO De
—
SF
Figure 5: FRI obtained considering different MSM with
varying density in the m From top to bottom: 13.9%
and PSN R= 23.50dB, 22.3% md PSNR = 21.9345,
32.5% and PSNR = 25.30dB (the PSNR were com-
puted for the whole images). Note that the middle image
is the same as the middle one displayed in Fig. 4
Mather, P., 1995. Computer Processing of Remotely-
Sensed Images: An Introduction. John Wiley & Sons,
Chichester (UK). ond eq.
Schiewe, J., 2002. Segmentation of high-resolution re-
motely sensed data - concepts, applications and problems.
Int. Arch. of Photogrammetry and Remote Sensing 34(4),
pp. 380—385.
Schowengerdt, R., 1997. Remote Sensing. Models and
Methods for Image Processing. Academic Press, San
Diego (USA).
Turiel, A. and del Pozo, A., 2002. Reconstructing images
from their most singular fractal set. IEEE Trans. on Im.
Proc. 11, pp. 345-350.
Turiel, A. and Parga, N., 2000. The multi-fractal structure
of contrast changes in natural images: from sharp edges to
textures. Neural Comp. 12, pp. 763—793.
Yocki, D., 1995. Image merging and data fusion by means
of the discrete 2D wavelet transform. J. Opt. Soc. Am. A
12(9), pp. 1834-1841.