TY - GEN
T1 - ToF meets RGB
T2 - 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
AU - Köhler, Thomas
AU - Haase, Sven
AU - Bauer, Sebastian
AU - Wasza, Jakob
AU - Kilgus, Thomas
AU - Maier-Hein, Lena
AU - Feußner, Hubertus
AU - Hornegger, Joachim
PY - 2013
Y1 - 2013
N2 - 3-D endoscopy is an evolving field of research with the intention to improve safety and efficiency of minimally invasive surgeries. Time-of-Flight (ToF) imaging allows to acquire range data in real-time and has been engineered into a 3-D endoscope in combination with an RGB sensor (640x480 px) as a hybrid imaging system, recently. However, the ToF sensor suffers from a low spatial resolution (64x48 px) and a poor signal-to-noise ratio. In this paper, we propose a novel multi-frame super-resolution framework to improve range images in a ToF/RGB multi-sensor setup. Our approach exploits high-resolution RGB data to estimate subpixel motion used as a cue for range super-resolution. The underlying non-parametric motion model based on optical flow makes the method applicable to endoscopic scenes with arbitrary endoscope movements. The proposed method was evaluated on synthetic and real images. Our approach improves the peak-signal-to-noise ratio by 1.6 dB and structural similarity by 0.02 compared to single-sensor super-resolution.
AB - 3-D endoscopy is an evolving field of research with the intention to improve safety and efficiency of minimally invasive surgeries. Time-of-Flight (ToF) imaging allows to acquire range data in real-time and has been engineered into a 3-D endoscope in combination with an RGB sensor (640x480 px) as a hybrid imaging system, recently. However, the ToF sensor suffers from a low spatial resolution (64x48 px) and a poor signal-to-noise ratio. In this paper, we propose a novel multi-frame super-resolution framework to improve range images in a ToF/RGB multi-sensor setup. Our approach exploits high-resolution RGB data to estimate subpixel motion used as a cue for range super-resolution. The underlying non-parametric motion model based on optical flow makes the method applicable to endoscopic scenes with arbitrary endoscope movements. The proposed method was evaluated on synthetic and real images. Our approach improves the peak-signal-to-noise ratio by 1.6 dB and structural similarity by 0.02 compared to single-sensor super-resolution.
UR - http://www.scopus.com/inward/record.url?scp=84894626844&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-40811-3_18
DO - 10.1007/978-3-642-40811-3_18
M3 - Conference contribution
C2 - 24505659
AN - SCOPUS:84894626844
SN - 9783642408106
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 139
EP - 146
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings
Y2 - 22 September 2013 through 26 September 2013
ER -