Semi-blind Spatially-Variant Deconvolution

Code for the pytorch implementation of "Semi-Blind Spatially-Variant Deconvolution in Optical Microscopy with Local Point Spread Function Estimation By Use Of Convolutional Neural Networks" https://ieeexplore.ieee.org/abstract/document/8451736 DOI: 10.1109/ICIP.2018.8451736 https://arxiv.org/abs/1803.07452

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.

Adaptations

The physical model for the PSF here is an anisotropic Gaussian distribution, implemented in gaussian_kernel. It can be adapted easily to other models. Software releases with other types of models will be available in the following months.

Requirements

The following python libraries are required. We advise the use of the conda package manager.

numpy scipy scikit-image pandas pytorch matplotlib

Howto

train.py is the file for training. test.py is the file for testing and deconvolution.

Citation

For any use of the code or parts of the code, please cite:

@INPROCEEDINGS{8451736, author={A. {Shajkofci} and M. {Liebling}}, booktitle={2018 25th IEEE International Conference on Image Processing (ICIP)}, title={Semi-Blind Spatially-Variant Deconvolution in Optical Microscopy with Local Point Spread Function Estimation by Use of Convolutional Neural Networks}, year={2018}, volume={}, number={}, pages={3818-3822},}

Licence

This is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License version 3 as published by the Free Software Foundation. Parts of the code are coming from the PyTorch project (https://github.com/pytorch/).