Departments:
- - Applied Informatics
- - Image Processing and Computer Graphics
- - Foundations of Computer Science
- - Computer Algorithms and Artificial Intelligence
- - Software Engineering
- - Research Group on Artificial Intelligence
[University of Szeged]
Discrete Image Reconstruction from Uncertain Data
 | Members: Péter Balázs, Mihály Gara, Zoltan Kato, Norbert Hantos Funded by
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Description
The goal of the project is to develop efficient image acquisition methods to gain visual information from incorrect, noisy, and uncertain projections. Such projections typically arise in sensor-network applications. Projection signals can be detected in various forms (X-ray, gamma-ray, radar, ultrasound, electric or electromagnetic sensors, etc.). Due to the large variety in the number, modality and position of the detectors, there is no general method to obtain an accurate reconstruction in each application. In the past years it turned out, that discrete tomography can guarantee reconstructions of good quality even when the classical reconstruction methods are hardly applicable due to the small number of possibly noisy projections. Our aim is to investigate the theoretical background of reconstruction problems where projections are noisy, and/or their direction is not known precisely. We study uniqueness and stability questions. We also design and implement discrete tomographic reconstruction methods for different applications and analyse the performance of those algorithms from the viewpoint of speed, accuracy, and noise-sensitivity, both theoretically and numerically.Publications
- Péter Balázs. Reconstruction of canonical hv-convex discrete sets from horizontal and vertical projections. In Proceedings of the International Workshop on Combinatorial Image Analysis, volume 5852 of Lecture Notes in Computer Science, pages 280-288, 2009. Springer Verlag.
- Norbert Hantos and Péter Balázs. Image Enhancement by Median Filters in Algebraic Reconstruction Methods: An Experimental Study. In Proceedings of International Symposium on Visual Computing, volume 6455 of Lecture Notes in Computer Science, Las Vegas, USA, pages 339-348, 2010. Springer Verlag.