Journal papers
Kyrollos Yanny; Kristina Monakhova; Richard W Shuai; Laura Waller
Deep learning for fast spatially varying deconvolution Journal Article
In: Optica, vol. 9, no. 1, pp. 96–99, 2022.
Abstract | Links | BibTeX | Tags: 3D imaging, Hyperspectral imaging; Image quality; Imaging systems; Neural networks; Reconstruction algorithms; Three dimensional reconstruction, spatially-varying
@article{Yanny:22,
title = {Deep learning for fast spatially varying deconvolution},
author = {Kyrollos Yanny and Kristina Monakhova and Richard W Shuai and Laura Waller},
url = {http://www.osapublishing.org/optica/abstract.cfm?URI=optica-9-1-96},
doi = {10.1364/OPTICA.442438},
year = {2022},
date = {2022-01-01},
journal = {Optica},
volume = {9},
number = {1},
pages = {96--99},
publisher = {OSA},
abstract = {Deconvolution can be used to obtain sharp images or volumes from blurry or encoded measurements in imaging systems. Given knowledge of the system's point spread function (PSF) over the field of view, a reconstruction algorithm can be used to recover a clear image or volume. Most deconvolution algorithms assume shift-invariance; however, in realistic systems, the PSF varies laterally and axially across the field of view due to aberrations or design. Shift-varying models can be used, but are often slow and computationally intensive. In this work, we propose a deep-learning-based approach that leverages knowledge about the system's spatially varying PSFs for fast 2D and 3D reconstructions. Our approach, termed MultiWienerNet, uses multiple differentiable Wiener filters paired with a convolutional neural network to incorporate spatial variance. Trained using simulated data and tested on experimental data, our approach offers a 625textminus1600texttimes increase in speed compared to iterative methods with a spatially varying model, and outperforms existing deep-learning-based methods that assume shift invariance.},
keywords = {3D imaging, Hyperspectral imaging; Image quality; Imaging systems; Neural networks; Reconstruction algorithms; Three dimensional reconstruction, spatially-varying},
pubstate = {published},
tppubtype = {article}
}
Deconvolution can be used to obtain sharp images or volumes from blurry or encoded measurements in imaging systems. Given knowledge of the system's point spread function (PSF) over the field of view, a reconstruction algorithm can be used to recover a clear image or volume. Most deconvolution algorithms assume shift-invariance; however, in realistic systems, the PSF varies laterally and axially across the field of view due to aberrations or design. Shift-varying models can be used, but are often slow and computationally intensive. In this work, we propose a deep-learning-based approach that leverages knowledge about the system's spatially varying PSFs for fast 2D and 3D reconstructions. Our approach, termed MultiWienerNet, uses multiple differentiable Wiener filters paired with a convolutional neural network to incorporate spatial variance. Trained using simulated data and tested on experimental data, our approach offers a 625textminus1600texttimes increase in speed compared to iterative methods with a spatially varying model, and outperforms existing deep-learning-based methods that assume shift invariance.