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: | |
| Added by: | [UNK] |
| Total mark: | 0 |
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