Improving Hybrid Quantum Annealing Tomographic Image Reconstruction with Regularization Strategies
Nau M, Vija AH, Reymann M, Gohn W, Maier A (2024)
Publication Type: Conference contribution, Original article
Publication year: 2024
Series: BVM Workshop
Pages Range: 3-8
Event location: Erlangen
Abstract
Quantum computing and quantum annealing present promising avenues for addressing complex problems in various fields, including tomographic image reconstruction. This study investigates the application of hybrid quantum annealing in the context of tomographic image reconstruction, focusing on the formulation of compatible conventional image regularization strategies: L2 and total variation. Using a Shepp-Logan phantom of image size 32× 32 with 4-bit grayscale encoding, we study the effect of the regularization techniques under the influence of their parameters and the runtime of the hybrid quantum annealer. The study reveals, that L2 regularization effectively enhances the obtained image reconstructions and total variation can further improve them. Despite efforts to employ regularized hybrid quantum annealing reconstructions, they still fall short in comparison to traditional reconstruction techniques.
Authors with CRIS profile
Merlin Nau
Lehrstuhl für Informatik 5 (Mustererkennung)
Maximilian Reymann
Lehrstuhl für Informatik 5 (Mustererkennung)
Andreas Maier
Lehrstuhl für Informatik 5 (Mustererkennung)
Involved external institutions
Germany (DE)How to cite
APA:
Nau, M., Vija, A.H., Reymann, M., Gohn, W., & Maier, A. (2024). Improving Hybrid Quantum Annealing Tomographic Image Reconstruction with Regularization Strategies. In Springer Fachmedien Wiesbaden (Eds.), Proceedings of the German Conference on Medical Image Computing - Bildverarbeitung für die Medizin (pp. 3-8). Erlangen.
MLA:
Nau, Merlin, et al. "Improving Hybrid Quantum Annealing Tomographic Image Reconstruction with Regularization Strategies." Proceedings of the German Conference on Medical Image Computing - Bildverarbeitung für die Medizin, Erlangen Ed. Springer Fachmedien Wiesbaden, 2024. 3-8.
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