Multi-Modal Super-Resolution with Deep Guided Filtering

Stimpel B, Syben-Leisner C, Schirrmacher F, Dörfler A, Maier A, Hölter P (2019)

Publication Type: Conference contribution

Publication year: 2019


Publisher: Springer Berlin Heidelberg

Pages Range: 110-115

Conference Proceedings Title: Informatik aktuell

Event location: Lübeck DE

ISBN: 9783658253257

DOI: 10.1007/978-3-658-25326-4_25


Despite the visually appealing results, most Deep Learningbased super-resolution approaches lack the comprehensibility that is required for medical applications. We propose a modified version of the locally linear guided filter for the application of super-resolution in medical imaging. The guidance map itself is learned end-to-end from multimodal inputs, while the actual data is only processed with known operators. This ensures comprehensibility of the results and simplifies the implementation of guarantees. We demonstrate the possibilities of our approach based on multi-modal MR and cross-modal CT and MR data. For both datasets, our approach performs clearly better than bicubic upsampling. For projection images, we achieve SSIMs of up to 0.99, while slice image data results in SSIMs of up to 0.98 for four-fold upsampling given an image of the respective other modality at full resolution. In addition, end-to-end learning of the guidance map considerably improves the quality of the results.

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Stimpel, B., Syben-Leisner, C., Schirrmacher, F., Dörfler, A., Maier, A., & Hölter, P. (2019). Multi-Modal Super-Resolution with Deep Guided Filtering. In Thomas M. Deserno, Andreas Maier, Christoph Palm, Heinz Handels, Klaus H. Maier-Hein, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 110-115). Lübeck, DE: Springer Berlin Heidelberg.


Stimpel, Bernhard, et al. "Multi-Modal Super-Resolution with Deep Guided Filtering." Proceedings of the Workshop on Bildverarbeitung fur die Medizin, 2019, Lübeck Ed. Thomas M. Deserno, Andreas Maier, Christoph Palm, Heinz Handels, Klaus H. Maier-Hein, Thomas Tolxdorff, Springer Berlin Heidelberg, 2019. 110-115.

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