ISPRS Commission III, Vol.34, Part 3A ,,Photogrammetric Computer Vision‘, Graz, 2002
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Figure 7: MIT building at various steps of image sharpness
(SNR=20).
We see in fig. 8 that the method works quite well even
on real data. The different sharpness of the three versions
of the image sharpness is recognized. The good resolving
power obtained for the ideal edge detector is plausible, as
the scanned original was of excellent quality.
4 CONCLUSIONS AND OUTLOOK
We have developed a procedure for blindly estimating the
point spread function. We define a contrast sensitivity func-
tion. This allows us to derive the resolving power as a
function of the PSF, the pixel size and the signal to noise
ratio. The PSF is assumed to be an anisotropic Gaussian
function. We estimate the corresponding scale matrix X)
from the local scale at automatically extracted edges. We
assume the image contains enough edges with different ori-
entations which result from very sharp edges in the scene.
The contrast sensitivity function which is based on an ideal
adaptive edge detection scheme for straight edges between
noisy homogeneous regions is derived. Experiments on
artificial and real data demonstrate the usefulness of the
approach.
The method is restricted to images with a sufficient number
of edges and to Gaussian shaped PSF. An extension to gen-
eral point spread functions is possible using tomographic
techniques, based on the Radon-transformation (cf. (Rosen-
feld and Kak, 1982)).
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Figure 8: Aerial images with various image sharpness.
Top: whole original image with image patch. Left: image
patch at various steps of sharpness. Right: edge histogram,
resolving power.
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