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Summary

This study introduces a data-driven approach for computational optical imaging using a lantern (COIL) microendoscopy. The new plug-and-play method enhances image reconstruction quality using learned denoisers.

Keywords:
data-driven priormulticore fiberphotonic lanternprimal–dual plug-and-play algorithm

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Area of Science:

  • Biomedical Optics
  • Computational Imaging
  • Machine Learning in Imaging

Background:

  • High-resolution in vivo imaging is crucial for biological processes.
  • Computational Optical Imaging using a Lantern (COIL) offers a single-pixel imaging solution.
  • Existing SARA-COIL methods rely on sparsity priors for image reconstruction.

Purpose of the Study:

  • To develop a data-driven approach for COIL microendoscopy.
  • To improve image reconstruction quality in COIL by integrating learned denoisers.
  • To validate the performance of the new method on simulated and real data.

Main Methods:

  • Developed a plug-and-play (PnP) algorithm by replacing the sparsity prior with a learned denoiser.
  • Utilized a proximal primal-dual algorithm to solve the inverse problem based on Morozov formulation.
  • Trained a neural network using learning theory to ensure desirable Lipschitz properties.

Main Results:

  • The PnP algorithm demonstrated improved image reconstruction quality compared to the variational SARA-COIL method.
  • Convergence of the primal-dual PnP algorithm to a solution of a monotone inclusion problem was shown.
  • Successful application on both simulated and real microendoscopy data.

Conclusions:

  • The data-driven PnP approach offers a significant advancement in COIL microendoscopy.
  • Learned denoisers provide a powerful alternative to traditional sparsity priors for image reconstruction.
  • This method enhances the potential of optical fibers for in vivo biological imaging.