Publications
Kristina Monakhova; Vi Tran; Grace Kuo; Laura Waller
Untrained networks for compressive lensless photography Journal Article
In: Opt. Express, vol. 29, no. 13, pp. 20913–20929, 2021.
Links | BibTeX | Tags: compressive imaging, compressive photography, high speed video, hyperspectral imaging, Hyperspectral imaging; Image quality; Imaging systems; Optical imaging; Phase imaging; Three dimensional imaging, learning-based
@article{Monakhova:21,
title = {Untrained networks for compressive lensless photography},
author = {Kristina Monakhova and Vi Tran and Grace Kuo and Laura Waller},
url = {http://www.opticsexpress.org/abstract.cfm?URI=oe-29-13-20913},
doi = {10.1364/OE.424075},
year = {2021},
date = {2021-06-01},
journal = {Opt. Express},
volume = {29},
number = {13},
pages = {20913--20929},
publisher = {OSA},
keywords = {compressive imaging, compressive photography, high speed video, hyperspectral imaging, Hyperspectral imaging; Image quality; Imaging systems; Optical imaging; Phase imaging; Three dimensional imaging, learning-based},
pubstate = {published},
tppubtype = {article}
}
Henry Pinkard; Hratch Baghdassarian; Adriana Mujal; Ed Roberts; Kenneth H Hu; Daniel Haim Friedman; Ivana Malenica; Taylor Shagam; Adam Fries; Kaitlin Corbin; others
Learned adaptive multiphoton illumination microscopy for large-scale immune response imaging Journal Article
In: Nature communications, vol. 12, no. 1, pp. 1–14, 2021.
Links | BibTeX | Tags: learning-based, microscopy, multiphoton
@article{pinkard2021learned,
title = {Learned adaptive multiphoton illumination microscopy for large-scale immune response imaging},
author = {Henry Pinkard and Hratch Baghdassarian and Adriana Mujal and Ed Roberts and Kenneth H Hu and Daniel Haim Friedman and Ivana Malenica and Taylor Shagam and Adam Fries and Kaitlin Corbin and others},
doi = {https://doi.org/10.1038/s41467-021-22246-5},
year = {2021},
date = {2021-01-01},
journal = {Nature communications},
volume = {12},
number = {1},
pages = {1--14},
publisher = {Nature Publishing Group},
keywords = {learning-based, microscopy, multiphoton},
pubstate = {published},
tppubtype = {article}
}
Michael Kellman; Kevin Zhang; Eric Markley; Jon Tamir; Emrah Bostan; Michael Lustig; Laura Waller
Memory-efficient Learning for Large-Scale Computational Imaging Journal Article
In: IEEE Transactions on Computational Imaging, vol. 6, pp. 1403-1414, 2020.
Links | BibTeX | Tags: algorithms, computational imaging, experimental design, learning-based, LED array, memory efficient, memory-efficient, physics-based
@article{kellman2020memory,
title = {Memory-efficient Learning for Large-Scale Computational Imaging},
author = { Michael Kellman and Kevin Zhang and Eric Markley and Jon Tamir and Emrah Bostan and Michael Lustig and Laura Waller},
url = {https://ieeexplore.ieee.org/document/9204455},
doi = {10.1109/TCI.2020.3025735},
year = {2020},
date = {2020-10-14},
journal = {IEEE Transactions on Computational Imaging},
volume = {6},
pages = {1403-1414},
keywords = {algorithms, computational imaging, experimental design, learning-based, LED array, memory efficient, memory-efficient, physics-based},
pubstate = {published},
tppubtype = {article}
}
Michael Kellman
Physics-based Learning for Large-scale Computational Imaging PhD Thesis
EECS Department, University of California, Berkeley, 2020.
Abstract | Links | BibTeX | Tags: algorithms, computational imaging, experimental design, learning-based, LED array, memory efficient, memory-efficient, physics-based
@phdthesis{Kellman:EECS-2020-167,
title = {Physics-based Learning for Large-scale Computational Imaging},
author = {Michael Kellman},
url = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2020/EECS-2020-167.html},
year = {2020},
date = {2020-08-01},
number = {UCB/EECS-2020-167},
school = {EECS Department, University of California, Berkeley},
abstract = {In computational imaging systems (e.g. tomographic systems, computational optics, magnetic resonance imaging) the acquisition of data and reconstruction of images are co-designed to retrieve information which is not traditionally accessible. The performance of such systems is characterized by how information is encoded to (forward process) and decoded from (inverse problem) the measurements. Recently, critical aspects of these systems, such as their signal prior, have been optimized using deep neural networks formed from unrolling the iterations of a physics-based image reconstruction.
In this dissertation, I will detail my work, physics-based learned design, to optimize the performance of the entire computational imaging system by jointly learning aspects of its experimental design and computational reconstruction. As an application, I introduce how the LED-array microscope performs super-resolved quantitative phase imaging and demonstrate how physics-based learning can optimize a reduced set of measurements without sacrificing performance to enable the imaging of live fast moving biology.
In this dissertation's latter half, I will discuss how to overcome some of the computational challenges encountered in applying physics-based learning concepts to large-scale computational imaging systems. I will describe my work, memory-efficient learning, that makes physics-based learning for large-scale systems feasible on commercially-available graphics processing units. I demonstrate this method on two large-scale real-world systems: 3D multi-channel compressed sensing MRI and super-resolution optical microscopy.},
keywords = {algorithms, computational imaging, experimental design, learning-based, LED array, memory efficient, memory-efficient, physics-based},
pubstate = {published},
tppubtype = {phdthesis}
}
In this dissertation, I will detail my work, physics-based learned design, to optimize the performance of the entire computational imaging system by jointly learning aspects of its experimental design and computational reconstruction. As an application, I introduce how the LED-array microscope performs super-resolved quantitative phase imaging and demonstrate how physics-based learning can optimize a reduced set of measurements without sacrificing performance to enable the imaging of live fast moving biology.
In this dissertation's latter half, I will discuss how to overcome some of the computational challenges encountered in applying physics-based learning concepts to large-scale computational imaging systems. I will describe my work, memory-efficient learning, that makes physics-based learning for large-scale systems feasible on commercially-available graphics processing units. I demonstrate this method on two large-scale real-world systems: 3D multi-channel compressed sensing MRI and super-resolution optical microscopy.
Emrah Bostan; Reinhard Heckel; Michael Chen; Michael Kellman; Laura Waller
Deep Phase Decoder: Self-calibrating phase microscopy with an untrained deep neural network Journal Article
In: Optica, vol. 7, no. 6, pp. 559-562, 2020.
Links | BibTeX | Tags: algorithms, learning-based, LED array, measurement diversity, phase imaging, physics-based, self-calibration
@article{bostan2020deep,
title = {Deep Phase Decoder: Self-calibrating phase microscopy with an untrained deep neural network},
author = { Emrah Bostan and Reinhard Heckel and Michael Chen and Michael Kellman and Laura Waller},
url = {https://doi.org/10.1364/OPTICA.389314
https://arxiv.org/abs/2001.09803},
doi = {10.1364/OPTICA.389314},
year = {2020},
date = {2020-05-21},
journal = {Optica},
volume = {7},
number = {6},
pages = {559-562},
keywords = {algorithms, learning-based, LED array, measurement diversity, phase imaging, physics-based, self-calibration},
pubstate = {published},
tppubtype = {article}
}
Kristina Monakhova; Joshua Yurtsever; Grace Kuo; Nick Antipa; Kyrollos Yanny; Laura Waller
Learned reconstructions for practical mask-based lensless imaging Journal Article
In: Optics express, vol. 27, no. 20, pp. 28075–28090, 2019.
Links | BibTeX | Tags: diffuser, learning-based, lensless imaging, physics-based
@article{monakhova2019learned,
title = {Learned reconstructions for practical mask-based lensless imaging},
author = { Kristina Monakhova and Joshua Yurtsever and Grace Kuo and Nick Antipa and Kyrollos Yanny and Laura Waller},
url = {https://doi.org/10.1364/OE.27.028075},
doi = {10.1364/OE.27.028075},
year = {2019},
date = {2019-09-30},
journal = {Optics express},
volume = {27},
number = {20},
pages = {28075--28090},
publisher = {Optical Society of America},
keywords = {diffuser, learning-based, lensless imaging, physics-based},
pubstate = {published},
tppubtype = {article}
}
Kristina Monakhova; Joshua Yurtsever; Grace Kuo; Nick Antipa; Kyrollos Yanny; Laura Waller
Unrolled, model-based networks for lensless imaging Journal Article
In: 2019.
Links | BibTeX | Tags: diffuser, learning-based, lensless imaging, physics-based
@article{monakhova2019unrolled,
title = {Unrolled, model-based networks for lensless imaging},
author = { Kristina Monakhova and Joshua Yurtsever and Grace Kuo and Nick Antipa and Kyrollos Yanny and Laura Waller},
url = {https://pdfs.semanticscholar.org/6a49/3ac2a0c8a3be888ece00b52bc1ec013df2bd.pdf},
year = {2019},
date = {2019-09-14},
keywords = {diffuser, learning-based, lensless imaging, physics-based},
pubstate = {published},
tppubtype = {article}
}
Michael Kellman; Emrah Bostan; Nicole A Repina; Laura Waller
Physics-based learned design: Optimized coded-illumination for quantitative phase imaging Journal Article
In: IEEE Transactions on Computational Imaging, vol. 5, no. 3, pp. 344–353, 2019.
Links | BibTeX | Tags: algorithm, experimental design, learning-based, phase imaging, physics-based
@article{kellman2019physics,
title = {Physics-based learned design: Optimized coded-illumination for quantitative phase imaging},
author = { Michael Kellman and Emrah Bostan and Nicole A Repina and Laura Waller},
url = {https://ieeexplore.ieee.org/document/8667888},
year = {2019},
date = {2019-09-01},
journal = {IEEE Transactions on Computational Imaging},
volume = {5},
number = {3},
pages = {344--353},
publisher = {IEEE},
keywords = {algorithm, experimental design, learning-based, phase imaging, physics-based},
pubstate = {published},
tppubtype = {article}
}
Emrah Bostan; Ulugbek S Kamilov; Laura Waller
Learning-based image reconstruction via parallel proximal algorithm Journal Article
In: IEEE Signal Processing Letters, vol. 25, no. 7, pp. 989–993, 2018.
Links | BibTeX | Tags: algorithms, fluorescence imaging, learning-based, regularization
@article{bostan2018learning,
title = {Learning-based image reconstruction via parallel proximal algorithm},
author = { Emrah Bostan and Ulugbek S Kamilov and Laura Waller},
url = {https://doi.org/10.1109/LSP.2018.2833812},
doi = {10.1109/LSP.2018.2833812},
year = {2018},
date = {2018-05-07},
journal = {IEEE Signal Processing Letters},
volume = {25},
number = {7},
pages = {989--993},
publisher = {IEEE},
keywords = {algorithms, fluorescence imaging, learning-based, regularization},
pubstate = {published},
tppubtype = {article}
}