QUALITY COMPARISON OF DIGITAL AND FILM-BASED IMAGES FOR
PHOTOGRAMMETRIC PURPOSES
Roland Perko! Andreas Klaus? Michael Gruber?
! Institute for Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Austria
email: perko@icg.tu-graz.ac.at
? VRVis Research Center, Inffeldgasse 16, 8010 Graz, Austria
email: klaus@vrvis.at
? Vexcel Imaging GmbH, MünzgrabenstraBe 11, 8010 Graz, Austria
email: mgruber@vexcel.co.at
KEY WORDS: Comparison, Digital, Analog, Sensors, Aerial.
ABSTRACT
Digital cameras are replacing analog film not only on the consumer market. New digital aerial cameras such as Vexcel
Imaging UltraCam, or Z/ DMC implement novel concepts that make the changeover to digital photogrammetry possible.
The comparison of image quality of these sensors is important when switching from analog to digital. In this paper
we propose algorithms of how to assess image quality, whereas the main focus is set to stereo matching which is the
fundamental for several photogrammetric procedures, like generation of digital elevation models or true orthophotos. We
use test image data from an experimental setup. We took images with a 11 megapixel CCD sensor and analog small
format camera with several types of film. The focal lengths of the used lenses are chosen in that way, that a 9j: digital
pixel (native CCD pixel size) represents the same object point as a pixel from a 20um film scan. With this constellation
we are able to show that the quality of a 9pm CCD pixels outperforms the quality of a 20m or less scanned film pixel.
The main disadvantage of analog film is its granularity that causes grain noise. To measure the impacts of grain noise to
image processing tasks, we use the following algorithms on artificial and natural images: Distances to the epipolar ray of
stereo matching results, Blonksi and Luxen edge response test, minimal radius of Siemens star and noise measurement
via entropy. In contrast to film images that feature a dynamic range of 8 bit, images captured with digital sensors feature
a high dynamic range of 12 bit and contain almost no noise. This makes the matching of poorly textured structures in
digitally sensed images possible with high accuracy, even when the matching in conventional film images fails. Stereo
matching on digital images results in a 2.5 times smaller noise level. The conclusion of the proposed work is that digital
sensors are leading to highly accurate and robust photogrammetric processing.
1 INTRODUCTION
New digital aerial cameras such as Vexcel Imaging Ultra-
Cam) (Leberl et al., 2003) or Z/I DMC (Hinz et al., 2000)
implement novel concepts that make the changeover to dig-
ital photogrammetry possible. In our previous work we
compared film-based images scanned with 15m with dig-
ital sensed images (Perko and Gruber, 2002). Now we
compare images taken from camera UltraCamp with film-
based images scanned at 5, 10, 15 and 204m. The focal
lengths of the used lenses are chosen in that way, that a
91m digital pixel (native CCD pixel size) represents the
same object point as a pixel from a 20,4m film scan.
For digital sensing we are using the camera UltraCamy,
with 100mm/ f : 5.6 apo digitar lens and 11 megapix-
els CCD sensor Dalsa TFT4027 with 9m pixelsize which
gives 12 bit radiometric resolution (denoted as ced in the
rest of this paper). To match the requirements that a film
pixel scanned at 20j/m equals a ced pixel of 9um the focal
20um r
length of the analog camera should be ffi, = SF fecd =
Sg Yum »
292 Omm.
Analog small format film images are taken using camera
Minolta Dynax 7 with Sigma 135-400mm/f1:4.5-5.6 lens
fixed at 222mm and then scanned with high precision scan-
1136
ner UltraScan 5000 (Vexcel Imaging Austria, 2002) at 5,
10, 15 and 20pm at dynamic range of 16 bit. Four small
format films Agfa APX 100 (Agfa, 2003), Ilford Delta 100
(Ilford, 2002), Kodak T-MA X (Kodak, 2002, Kodak, 2004)
and Kodak T-PAN (Kodak, 2003) were used (denoted as
apx, delta, t-max and t-pan in the rest of this paper). Both
cameras are geometrically calibrated with high accuracy.
The paper is structured as follows. First, we propose al-
gorithms of how to assess image quality (section 2). Next,
results are given in section 3. Finally, concluding remarks
are made in section 4.
2 TESTING METHODS
We propose three tests to evaluate the geometric accuracy
of images, namely image matching, edge response and Sie-
mens star test and one test for noise measurement.
2.1 Image matching
To evaluate the geometric accuracy of images, we propose
a stereo image matching setup. Homologous points of two
images taken from the same device from different spots
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