Semi-blind spatially-variant deconvolution in optical microscopy with local Point Spread Function estimation by use of Convolutional Neural Networks
| Type of publication: | Idiap-RR |
| Citation: | Shajkofci_Idiap-RR-07-2018 |
| Number: | Idiap-RR-07-2018 |
| Year: | 2018 |
| Month: | 6 |
| 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. |
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| Projects: |
Idiap |
| Authors: | |
| Crossref by |
Shajkofci_ICIP2018_2018 |
| Added by: | [ADM] |
| Total mark: | 0 |
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