3D Image Reconstruction from Multi-Focus Microscope: Axial Super-Resolution and Multiple-Frame Processing
Seunghwan Yoo, Pablo Ruiz, Xiang Huang, Kuan He, Nicola J. Ferrier, Mark Hereld, Alan Selewa, Matthew Daddysman
Abstract
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.
BibTeX
@inproceedings{icassp2018_3dimagereconstru,
title = {3D Image Reconstruction from Multi-Focus Microscope: Axial Super-Resolution and Multiple-Frame Processing},
author = {Seunghwan Yoo and Pablo Ruiz and Xiang Huang and Kuan He and Nicola J. Ferrier and Mark Hereld and Alan Selewa and Matthew Daddysman and Norbert Scherer and Oliver Cossairt and Aggelos K. Katsaggelos},
booktitle = {ICASSP 2018},
year = {2018}
}