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Image Reconstruction in Light-Sheet Microscopy: Spatially Varying Deconvolution and Mixed Noise.

Bogdan Toader1,2,3, Jérôme Boulanger4, Yury Korolev2

  • 1Cambridge Advanced Imaging Centre, University of Cambridge, Anatomy School, Downing Street, Cambridge, CB2 3DY UK.

Journal of Mathematical Imaging and Vision
|November 4, 2022
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Summary

This study introduces a novel deconvolution method for light-sheet microscopy, effectively addressing spatially varying blur and combined noise. The new approach significantly improves image reconstruction quality for microscopy data.

Keywords:
DeconvolutionLight-sheet microscopyNumerical methodsPoisson and Gaussian noisePrimal–dual hybrid gradient

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Area of Science:

  • Microscopy
  • Image Processing
  • Computational Science

Background:

  • Light-sheet microscopy data is often degraded by spatially varying blur.
  • Microscopy images are affected by a combination of Poisson and Gaussian noise.
  • The point spread function (PSF) in light-sheet microscopy varies due to excitation sheet and detection objective interactions.

Purpose of the Study:

  • To develop an advanced deconvolution technique for light-sheet microscopy.
  • To model the image formation process considering the interaction between excitation and detection.
  • To create a robust variational model for handling mixed noise types.

Main Methods:

  • A new image formation model is proposed, incorporating the excitation-detection interaction.
  • A variational model is formulated using an infimal convolution of noise fidelities to manage Poisson and Gaussian noise.
  • The primal-dual hybrid gradient (PDHG) algorithm is adapted for efficient inverse problem solving.

Main Results:

  • Convergence rates and a discrepancy principle are established for the infimal convolution fidelity.
  • The proposed method demonstrates superior image reconstruction compared to existing techniques.
  • Successful validation on both simulated and real light-sheet microscopy data.

Conclusions:

  • The developed deconvolution method effectively handles spatially varying blur and mixed noise in light-sheet microscopy.
  • The novel variational approach and PDHG algorithm application offer significant improvements in image quality.
  • This work advances the capabilities of quantitative imaging in light-sheet microscopy.