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 [BibTeX] [Marc21]
Semi-Blind Spatially-Variant Deconvolution in Optical Microscopy with Local Point Spread Function Estimation by Use of Convolutional Neural Networks
Type of publication: Conference paper
Citation: Shajkofci_ICIP2018_2018
Publication status: Published
Booktitle: 2018 25th IEEE International Conference on Image Processing (ICIP)
Year: 2018
Month: September
Pages: 3818-3822
Publisher: IEEE
ISSN: 2381-8549
ISBN: 978-1-4799-7061-2
Crossref: Shajkofci_Idiap-RR-07-2018:
DOI: 10.1109/ICIP.2018.8451736
Abstract: We present a semi-blind, spatially-variant deconvolution technique aimed at optical microscopy that combines a local estimation step of the point spread function (PSF) and deconvolution using a spatially variant, regularized Richardson-Lucy algorithm. To find the local PSF map in a computationally tractable way, we train a convolutional neural network to perform regression of an optical parametric model on synthetically blurred image patches. We deconvolved both synthetic and experimentally-acquired data, and achieved an improvement of image SNR of 1.00 dB on average, compared to other deconvolution algorithms.
Keywords: blind deconvolution, Convolutional Neural Networks, microscopy, point spread function
Projects Idiap
Authors Shajkofci, Adrian
Liebling, Michael
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Total mark: 0
  • Shajkofci_ICIP2018_2018.pdf