Multi-sensor super-resolution for hybrid range imaging with application to 3-D endoscopy and open surgery
In this paper, we propose a multi-sensor super-resolution framework for hybrid imaging to super-resolve data from one modality by taking advantage of additional guidance images of a complementary modality. This concept is applied to hybrid 3-D range imaging in image-guided surgery, where high-qualit...
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| Hauptverfasser: | , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
3 July 2015
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| In: |
Medical image analysis
Year: 2015, Jahrgang: 24, Heft: 1, Pages: 220-234 |
| ISSN: | 1361-8423 |
| DOI: | 10.1016/j.media.2015.06.011 |
| Online-Zugang: | Resolving-System, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.media.2015.06.011 Verlag, lizenzpflichtig, Volltext: http://www.sciencedirect.com/science/article/pii/S1361841515000985 
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| Verfasserangaben: | Thomas Köhler, Sven Haase, Sebastian Bauer, Jakob Wasza, Thomas Kilgus, Lena Maier-Hein, Christian Stock, Joachim Hornegger, Hubertus Feußner |
| Zusammenfassung: | In this paper, we propose a multi-sensor super-resolution framework for hybrid imaging to super-resolve data from one modality by taking advantage of additional guidance images of a complementary modality. This concept is applied to hybrid 3-D range imaging in image-guided surgery, where high-quality photometric data is exploited to enhance range images of low spatial resolution. We formulate super-resolution based on the maximum a-posteriori (MAP) principle and reconstruct high-resolution range data from multiple low-resolution frames and complementary photometric information. Robust motion estimation as required for super-resolution is performed on photometric data to derive displacement fields of subpixel accuracy for the associated range images. For improved reconstruction of depth discontinuities, a novel adaptive regularizer exploiting correlations between both modalities is embedded to MAP estimation. We evaluated our method on synthetic data as well as ex-vivo images in open surgery and endoscopy. The proposed multi-sensor framework improves the peak signal-to-noise ratio by 2 dB and structural similarity by 0.03 on average compared to conventional single-sensor approaches. In ex-vivo experiments on porcine organs, our method achieves substantial improvements in terms of depth discontinuity reconstruction. |
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| Beschreibung: | Gesehen am 15.07.2020 |
| Beschreibung: | Online Resource |
| ISSN: | 1361-8423 |
| DOI: | 10.1016/j.media.2015.06.011 |