Proceedings Article | 18 May 2012
Proc. SPIE. 8355, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXIII
KEYWORDS: Super resolution, Reconstruction algorithms, Image resolution, Modulation transfer functions, Lawrencium, Signal to noise ratio, Image enhancement, Minimum resolvable temperature difference, Image processing, Cameras
For many military operations situational awareness is of great importance. This situational awareness and related
tasks such as Target Acquisition can be acquired using cameras, of which the resolution is an important characteristic.
Super resolution reconstruction algorithms can be used to improve the effective sensor resolution. In order
to judge these algorithms and the conditions under which they operate best, performance evaluation methods
are necessary. This evaluation, however, is not straightforward for several reasons. First of all, frequency-based
evaluation techniques alone will not provide a correct answer, due to the fact that they are unable to discriminate
between structure-related and noise-related effects. Secondly, most super-resolution packages perform additional
image enhancement techniques such as noise reduction and edge enhancement. As these algorithms improve
the results they cannot be evaluated separately. Thirdly, a single high-resolution ground truth is rarely available.
Therefore, evaluation of the differences in high resolution between the estimated high resolution image
and its ground truth is not that straightforward. Fourth, different artifacts can occur due to super-resolution
reconstruction, which are not known on forehand and hence are difficult to evaluate.
In this paper we present a set of new evaluation techniques to assess super-resolution reconstruction algorithms.
Some of these evaluation techniques are derived from processing on dedicated (synthetic) imagery.
Other evaluation techniques can be evaluated on both synthetic and natural images (real camera data). The
result is a balanced set of evaluation algorithms that can be used to assess the performance of super-resolution
reconstruction algorithms.