ArticleJournal of mathematical imaging and vision2022
Image Reconstruction in Light-Sheet Microscopy: Spatially Varying Deconvolution and Mixed Noise.
Article in Journal of mathematical imaging and vision, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed, 23 citations in OpenAlex.
- Live imaging of late-stage preimplantation human embryos reveals de novo mitotic errors.Nature biotechnology · 2026Article
- A physics-informed alternative to Richardson-Lucy deconvolution across SNR regimes without iteration cutoffs.Nature communications · 2026Article
- Computational Super-Resolution: An Odyssey in Harnessing Priors to Enhance Optical Microscopy Resolution.Analytical chemistry · 2025Review
- Hybrid Space Calibrated 3D Network of Diffractive Hyperspectral Optical Imaging Sensor.Sensors (Basel, Switzerland) · 2024Article
- 3D exploration of gene expression in chicken embryos through combined RNA fluorescence in situ hybridization, immunofluorescence, and clearing.BMC biology · 2024Article
- Distortion Correction and Denoising of Light Sheet Fluorescence Images.Sensors (Basel, Switzerland) · 2024Article
- Article
- Blind deconvolution in autocorrelation inversion for multiview light-sheet microscopy.Microscopy research and technique · 2022Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
Abstract
We study the problem of deconvolution for light-sheet microscopy, where the data is corrupted by spatially varying blur and a combination of Poisson and Gaussian noise. The spatial variation of the point spread function of a light-sheet microscope is determined by the interaction between the excitation sheet and the detection objective PSF. We introduce a model of the image formation process that incorporates this interaction and we formulate a variational model that accounts for the combination of Poisson and Gaussian noise through a data fidelity term consisting of the infimal convolution of the single noise fidelities, first introduced in L. Calatroni et al. (SIAM J Imaging Sci 10(3):1196-1233, 2017). We establish convergence rates and a discrepancy principle for the infimal convolution fidelity and the inverse problem is solved by applying the primal-dual hybrid gradient (PDHG) algorithm in a novel way. Numerical experiments performed on simulated and real data show superior reconstruction results in comparison with other methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.