4.8 Article

Learning Optimal Wavefront Shaping for Multi-Channel Imaging

Journal

Publisher

IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2021.3076873

Keywords

Imaging; Three-dimensional displays; Microscopy; Location awareness; Optical microscopy; Optical imaging; Optical diffraction; Computational microscopy; wavefront coding; deep neural networks; end-to-end optimization

Funding

  1. European Union's Horizon 2020 Research and Innovation Programe [802567-ERC-5D-NanoTrack]
  2. Israel Science Foundation [852/17, 450/18]
  3. Zuckerman STEM Leadership Program
  4. Google Faculty Research Award for Machine Perception
  5. Technion Ollendorff Minerva Center

Ask authors/readers for more resources

Fast acquisition of depth information is crucial for accurate 3D tracking, and wavefront coding can improve the precision of 3D tracking. Multi-channel wavefront coding has better performance in low-light applications and outperforms traditional single-channel designs.
Fast acquisition of depth information is crucial for accurate 3D tracking of moving objects. Snapshot depth sensing can be achieved by wavefront coding, in which the point-spread function (PSF) is engineered to vary distinctively with scene depth by altering the detection optics. In low-light applications, such as 3D localization microscopy, the prevailing approach is to condense signal photons into a single imaging channel with phase-only wavefront modulation to achieve a high pixel-wise signal to noise ratio. Here we show that this paradigm is generally suboptimal and can be significantly improved upon by employing multi-channel wavefront coding, even in low-light applications. We demonstrate our multi-channel optimization scheme on 3D localization microscopy in densely labelled live cells where detectability is limited by overlap of modulated PSFs. At extreme densities, we show that a split-signal system, with end-to-end learned phase masks, doubles the detection rate and reaches improved precision compared to the current state-of-the-art, single-channel design. We implement our method using a bifurcated optical system, experimentally validating our approach by snapshot volumetric imaging and 3D tracking of fluorescently labelled subcellular elements in dense environments.

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