%Aigaion2 BibTeX export from Idiap Publications
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@INPROCEEDINGS{Shajkofci_ICIP2018_2018,
         author = {Shajkofci, Adrian and Liebling, Michael},
       keywords = {blind deconvolution, Convolutional Neural Networks, microscopy, point spread function},
       projects = {Idiap},
          month = sep,
          title = {Semi-Blind Spatially-Variant Deconvolution in Optical Microscopy with Local Point Spread Function Estimation by Use of Convolutional Neural Networks},
      booktitle = {2018 25th IEEE International Conference on Image Processing (ICIP)},
           year = {2018},
          pages = {3818-3822},
      publisher = {IEEE},
           issn = {2381-8549},
           isbn = {978-1-4799-7061-2},
            doi = {10.1109/ICIP.2018.8451736},
       crossref = {Shajkofci_Idiap-RR-07-2018},
       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.},
            pdf = {https://publications.idiap.ch/attachments/papers/2018/Shajkofci_ICIP2018_2018.pdf}
}



crossreferenced publications: 
@TECHREPORT{Shajkofci_Idiap-RR-07-2018,
         author = {Shajkofci, Adrian and Liebling, Michael},
       projects = {Idiap},
          month = {6},
          title = {Semi-blind spatially-variant deconvolution in optical microscopy with local Point Spread Function estimation by use of Convolutional Neural Networks},
           type = {Idiap-RR},
         number = {Idiap-RR-07-2018},
           year = {2018},
    institution = {Idiap},
           note = {Accepted to IEEE ICIP 2018},
       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.},
            pdf = {https://publications.idiap.ch/attachments/reports/2018/Shajkofci_Idiap-RR-07-2018.pdf}
}