ISPRS Commission III, Vol.34, Part 3A „Photogrammetric Computer Vision“, Graz, 2002
CHARACTERIZING IMAGE QUALITY:
BLIND ESTIMATION OF THE POINT SPREAD FUNCTION FROM A SINGLE IMAGE
Marc Luxen, Wolfgang Forstner
Institute for Photogrammetry, University of Bonn, Germany
luxen—wf@ipb.uni-bonn.de
KEY WORDS: Characterization of algorithms, contrast sensitivity function (CSF), image sharpness, modulation transfer
function (MTF), point spread function (PSF), scale, resolving power
ABSTRACT
This paper describes a method for blind estimation of sharpness and resolving power from a single image. These measures
can be used to characterize images in the context of the performance of image analysis procedures. The method assumes
the point spread function (PSF) can be approximated by an anisotropic Gaussian. The width o of the PSF is determined
by the ratio 5/0, of the standard deviations of the intensity and of its derivative at edges. The contrast sensitivity
function (CSF) is based on an optimal model for detecting straight edges between homogeneous regions in noisy images.
It depends on the signal to noise ratio and is linear in the frequency. The method is applied to artificial and real images
proving that it gives valuable results.
1 INTRODUCTION
The usability of images for interpretation, orientation or
object reconstruction purposes highly depends on the im-
age quality. In principle it makes no difference whether
image analysis is performed manually by a human opera-
tor or whether digital images are analyzed automatically:
The reliability, accuracy and precision of results of image
analysis procedures directly is influenced by the quality of
the underlying image data.
Image quality can be characterized by a large number of
measures, e. g. contrast, brightness, noise variance, sharp-
ness, radiometric resolution, granularity, point spread func-
tion (PSF), modulation and contrast transfer function (MTF,
CTF), resolving power, etc. (cf. (Lei and Tiziani, 1989),
(Zieman, 1997)), all referring to the radiometry of the im-
ages.
As aerial cameras and films are designed to obtain high-
est image quality, the user, based on his/her experience
normally just decides on whether the images can be used
or not, e. g. due to motion blur. In the following pro-
cess, image quality is not referred to using classical qual-
ity measures. With digital or digitized images the situation
changes, especially because automatic image analysis pro-
cedures can be applied and their performance can be much
better described as a function of image quality.
In (Fórstner, 1996) it is shown that the performance char-
acteristics of vision algorithms can be used to select the set
(a, t) of algorithms a with tuning parameters £ applied to
image data d leading to a quality q(r|d, a, t) of the result r
from
(à,t) = {(a, t)|P(a(r|d, a,t) > qo) > Po}
Thus the probability P of obtaining a quality q being better
than a pre-specified minimum quality qo should be larger
than a pre-specified minimum probability Fp. The most
difficult part in evaluating this equation is the characteriza-
tion of the domain D of all the images d which one expects.
Therefore one needs to be able to characterize images to
that extent which is relevant for the task of performance
characterization or more specifically for the selection of
appropriate algorithms a and tuning parameters t. As an
example, fig. 1 shows the effect of two different edge de-
tectors on two aerial images of different sharpness. The
final goal would be to predict the quality of the result of
these edge detectors as a function of the image sharpness
as one of the decisive parameters.
left: n n BENNO with aW g 9
Figure 1: Effect of two different edge detectors on aerial
images of different sharpness. The same parameters were
taken for both images, no attempt was made to obtain the
best results in all four cases.
Among other measures, such as power spectrum or edge
density, image sharpness is important for characterizing
images. Image blur, which limits the visibility of details,
can be objectively measured by the point spread function
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