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Updated: Apr 15, 2026

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
Published on: February 12, 2014
3D multifocus astigmatism and compressed sensing (3D MACS) based superresolution reconstruction
Jiaqing Huang1, Mingzhai Sun2, Kristyn Gumpper3
1Department of Surgery, Davis Heart and Lung Research Institute, The Ohio State University, Columbus, OH, 43210, USA ; Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH, 43210, USA ; These authors contribute equally to this work.
Abstract:
Single molecule based superresolution techniques (STORM/PALM) achieve nanometer spatial resolution by integrating the temporal information of the switching dynamics of fluorophores (emitters). When emitter density is low for each frame, they are located to the nanometer resolution. However, when the emitter density rises, causing significant overlapping, it becomes increasingly difficult to accurately locate individual emitters. This is particularly apparent in three dimensional (3D) localization because of the large effective volume of the 3D point spread function (PSF). The inability to precisely locate the emitters at a high density causes poor temporal resolution of localization-based superresolution technique and significantly limits its application in 3D live cell imaging. To address this problem, we developed a 3D high-density superresolution imaging platform that allows us to precisely locate the positions of emitters, even when they are significantly overlapped in three dimensional space. Our platform involves a multi-focus system in combination with astigmatic optics and an ℓ 1-Homotopy optimization procedure. To reduce the intrinsic bias introduced by the discrete formulation of compressed sensing, we introduced a debiasing step followed by a 3D weighted centroid procedure, which not only increases the localization accuracy, but also increases the computation speed of image reconstruction. We implemented our algorithms on a graphic processing unit (GPU), which speeds up processing 10 times compared with central processing unit (CPU) implementation. We tested our method with both simulated data and experimental data of fluorescently labeled microtubules and were able to reconstruct a 3D microtubule image with 1000 frames (512×512) acquired within 20 seconds.
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