Projects

Microscopy PSF Computation and Estimation

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The point-spread function (PSF) plays a fundamental role in fluorescence microscopy. An accurately calculated and estimated PSF model can significantly improve the performance in 3D deconvolution microscopy and also the localization accuracy in single-molecule microscopy. In this project, we we present some approaches to:

  • estimate the Gaussian blur for the confocal microscopy [1];
  • computate and estimate the Gibson-Lanni model in the wide-field microscopy [2-3];
Publications:
  1. J. Li, F. Xue, F. Qu, Y.-P. Ho and T. Blu, “On-the-fly estimation of a microscopy point spread function”, Opt. Express, vol. 26, no. 20, pp. 26120-26133, 2018. [PDF]

  2. J. Li, F. Xue and T. Blu, “Gaussian blur estimation for photon-limited images”, 2017 24th Proc. IEEE Int. Conf. on Image Processing (ICIP 2017), Beijing, China, 2017, pp. 495-499. [PDF] [Slides]

  3. J. Li, F. Xue and T. Blu, “Fast and accurate three-dimensional point spread function computation for fluorescence microscopy”, J. Opt. Soc. Am. A, vol. 34, no. 6, pp. 1029-1034, 2017. [Link] [PDF] [Slides] [Demo] [Matlab Code] [ImageJ Plugin] [Icy Plugin] [Python Code] (Top Downloaded Article in Oct 2017)
  4. J. Li, F. Xue and T. Blu, “Accurate 3D PSF estimation from a wide-field microscopy image”, 2018 15th Proc. IEEE Int. Symp. Biomed. Imaging (ISBI 2018), Washington, D.C., USA, 2018, pp. 501-504. [Link] [PDF] [Slides]

3D Deconvolution Microscopy

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3D deconvolution microscopy is a powerful tool to improve the quality of fluorescence microscopy images. It can be applied to several microscopy techniques, for example the conventional wide-field microscopy, confocal microscopy, structured illumination microscopy (SIM), or the localization microscopy. We present an efficient approach for the deconvolution of 3D fluorescence microscopy images based on the PURE-LET algorithm.

Publications:

  1. J. Li, F. Luisier and T. Blu, “PURE-LET deconvolution of 3D fluorescence microscopy images”, 2017 14th Proc. IEEE Int. Symp. Biomed. Imaging (ISBI 2017), Melbourne, Australia, 2017, pp. 723-727. [Link] [PDF] [Slides] [Demo] [Codes] (Best Student Paper Award, 2nd place)

Poissonian Image Deconvolution overview

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Images are often corrupted by noise and blurring during the acquisition process. In a variety of applications, ranging from astronomical imaging to biological microscopy, two predominant sources of noise (Poisson and mixed Poisson-Gaussian) are often considered during the acquisition process. We parametrize the deconvolution process as a linear combination of elementary functions, and then optimized by minimizing a robust estimate of the true mean squared error based on the noise statistics.

Publications:

  1. J. Li, F. Luisier and T. Blu, “PURE-LET image deconvolution”, IEEE Trans. Image Process., vol. 27, no. 1, pp. 92-105, 2018. [Link] [PDF] [Suppl.] [Codes] [ImageJ Plugin]
  2. J. Li, F. Luisier and T. Blu, “Deconvolution of Poissonian images with the PURE-LET approach”, 2016 23rd Proc. IEEE Int. Conf. on Image Processing (ICIP 2016), Phoenix, Arizona, USA, 2016, pp. 2708-2712. [Link] [PDF] [Slides] [Poster] [Codes] (Best Paper Runner-up Award)

Particle Tracking and Dynamic Analysis

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Accurate and robust spot tracking is a necessary tool for quantitative motion analysis in fluorescence microscopy images. Few trackers however consider the underlying dynamics present in biological systems. We propose a multi-frame tracker that exploits this stationary motion, and then perform the analysis of the protein dynamics.

Publications:

  1. J. Li, C. Gilliam and T. Blu, “A multi-frame optical flow spot tracker”, 2015 22nd Proc. IEEE Int. Conf. on Image Processing (ICIP 2015), Québec City, Canada, 2015, pp. 3670-3674. [Link] [PDF] [Poster] [Demo]