ArticleBiomedical optics express2020
Deconvolution for multimode fiber imaging: modeling of spatially variant PSF.
Article in Biomedical optics express, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Enhancing volumetric microscopy with blind computational correction of spatially variant aberrations using neural field representation.bioRxiv : the preprint server for biology · 2026Article
- Ring deconvolution microscopy: exploiting symmetry for efficient spatially varying aberration correction.Nature methods · 2025Article
- Demixing fluorescence time traces transmitted by multimode fibers.Nature communications · 2024Article
- Beyond synthetic aperture focusing: deconvolution-based elevation resolution enhancement using simulated point spread function for linear array-based three-dimensional photoacoustic imaging.Biomedical optics express · 2024Article
- Large depth-of-field ultra-compact microscope by progressive optimization and deep learning.Nature communications · 2023Article
- Repeated imaging through a multimode optical fiber using adaptive optics.Biomedical optics express · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Focusing light through a step-index multimode optical fiber (MMF) using wavefront control enables minimally-invasive endoscopy of biological tissue. The point spread function (PSF) of such an imaging system is spatially variant, and this variation limits compensation for blurring using most deconvolution algorithms as they require a uniform PSF. However, modeling the spatially variant PSF into a series of spatially invariant PSFs re-opens the possibility of deconvolution. To achieve this we developed svmPSF: an open-source Java-based framework compatible with ImageJ. The approach takes a series of point response measurements across the field-of-view (FOV) and applies principal component analysis to the measurements' co-variance matrix to generate a PSF model. By combining the svmPSF output with a modified Richardson-Lucy deconvolution algorithm, we were able to deblur and regularize fluorescence images of beads and live neurons acquired with a MMF, and thus effectively increasing the FOV.
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Registered trials
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