%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}
}