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AutoDeconJ: a GPU accelerated ImageJ plugin for 3D light field deconvolution with optimal iteration numbers predicting - MaRDI portal

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AutoDeconJ: a GPU accelerated ImageJ plugin for 3D light field deconvolution with optimal iteration numbers predicting

From MaRDI portal
Publication:6408674

arXiv2208.11422MaRDI QIDQ6408674

Author name not available (Why is that?)

Publication date: 24 August 2022

Abstract: Light field microscopy is a compact solution to high-speed 3D fluorescence imaging. Usually, we need to do 3D deconvolution to the captured raw data. Although there are deep neural network methods that can accelerate the reconstruction process, the model is not universally applicable for all system parameters. Here, we develop AutoDeconJ, a GPU accelerated ImageJ plugin for 4.4x faster and accurate deconvolution of light field microscopy data. We further propose an image quality metric for the deconvolution process, aiding in automatically determining the optimal number of iterations with higher reconstruction accuracy and fewer artifacts




Has companion code repository: https://github.com/onetism/autodeconj








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