TY - GEN
T1 - 3D Image Reconstruction from Multi-Focus Microscope
T2 - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
AU - Yoo, Seunghwan
AU - Ruiz, Pablo
AU - Huang, Xiang
AU - He, Kuan
AU - Ferrier, Nicola J.
AU - Hereld, Mark
AU - Selewa, Alan
AU - Daddysman, Matthew
AU - Scherer, Norbert
AU - Cossairt, Oliver Strides
AU - Katsaggelos, Aggelos K
N1 - Funding Information:
This work was supported as part of the Small Worlds project by funding through the Biological Systems Science Division, Office of Biological and Environmental Research, Office of Science, U.S. Dept. of Energy, under Contract DE-AC02-06CH11357.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/10
Y1 - 2018/9/10
N2 - Multi-focus microscope (MFM) provides a way to obtain 3D information by simultaneously capturing multiple focal planes. The naive method for MFM reconstruction is to stack the sub-images with alignment. However, the resolution in the z-axis in this method is limited by the number of acquired focal planes. In this work we build on a recent reconstruction algorithm for MFM, using information from multiple frames to improve the reconstruction quality. We propose two multiple-frame MFM image reconstruction algorithms: batch and recursive approaches. In the batch approach, we take multiple MFM frames and jointly estimate the 3D image and the motion for each frame. In the recursive approach, we utilize the reconstructed image from the previous frame. Experimental results show that the proposed algorithms produce a sequence of 3D object reconstruction with high quality that enable reconstruction of dynamic extended objects.
AB - Multi-focus microscope (MFM) provides a way to obtain 3D information by simultaneously capturing multiple focal planes. The naive method for MFM reconstruction is to stack the sub-images with alignment. However, the resolution in the z-axis in this method is limited by the number of acquired focal planes. In this work we build on a recent reconstruction algorithm for MFM, using information from multiple frames to improve the reconstruction quality. We propose two multiple-frame MFM image reconstruction algorithms: batch and recursive approaches. In the batch approach, we take multiple MFM frames and jointly estimate the 3D image and the motion for each frame. In the recursive approach, we utilize the reconstructed image from the previous frame. Experimental results show that the proposed algorithms produce a sequence of 3D object reconstruction with high quality that enable reconstruction of dynamic extended objects.
KW - 3D image reconstruction
KW - Multi-focus microscopy
KW - Multi-frame image reconstruction
KW - Total variation
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U2 - 10.1109/ICASSP.2018.8462234
DO - 10.1109/ICASSP.2018.8462234
M3 - Conference contribution
AN - SCOPUS:85054246055
SN - 9781538646588
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 1453
EP - 1457
BT - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 15 April 2018 through 20 April 2018
ER -