Publications
Bahram Javidi; Artur Carnicer; Jun Arai; Toshiaki Fujii; Hong Hua; Hongen Liao; Manuel Martínez-Corral; Filiberto Pla; Adrian Stern; Laura Waller; Qiong-Hua Wang; Gordon Wetzstein; Masahiro Yamaguchi; Hirotsugu Yamamoto
Roadmap on 3D integral imaging: sensing, processing, and display Journal Article
In: Opt. Express, vol. 28, no. 22, pp. 32266–32293, 2020.
Abstract | Links | BibTeX | Tags: Holographic displays; Image processing; Image quality; Integral photography; Low light levels; Spatial light modulators
@article{Javidi:20,
title = {Roadmap on 3D integral imaging: sensing, processing, and display},
author = {Bahram Javidi and Artur Carnicer and Jun Arai and Toshiaki Fujii and Hong Hua and Hongen Liao and Manuel Martínez-Corral and Filiberto Pla and Adrian Stern and Laura Waller and Qiong-Hua Wang and Gordon Wetzstein and Masahiro Yamaguchi and Hirotsugu Yamamoto},
url = {http://www.opticsexpress.org/abstract.cfm?URI=oe-28-22-32266},
doi = {10.1364/OE.402193},
year = {2020},
date = {2020-10-01},
journal = {Opt. Express},
volume = {28},
number = {22},
pages = {32266--32293},
publisher = {OSA},
abstract = {This Roadmap article on three-dimensional integral imaging provides an overview of some of the research activities in the field of integral imaging. The article discusses various aspects of the field including sensing of 3D scenes, processing of captured information, and 3D display and visualization of information. The paper consists of a series of 15 sections from the experts presenting various aspects of the field on sensing, processing, displays, augmented reality, microscopy, object recognition, and other applications. Each section represents the vision of its author to describe the progress, potential, vision, and challenging issues in this field.},
keywords = {Holographic displays; Image processing; Image quality; Integral photography; Low light levels; Spatial light modulators},
pubstate = {published},
tppubtype = {article}
}
This Roadmap article on three-dimensional integral imaging provides an overview of some of the research activities in the field of integral imaging. The article discusses various aspects of the field including sensing of 3D scenes, processing of captured information, and 3D display and visualization of information. The paper consists of a series of 15 sections from the experts presenting various aspects of the field on sensing, processing, displays, augmented reality, microscopy, object recognition, and other applications. Each section represents the vision of its author to describe the progress, potential, vision, and challenging issues in this field.
Fanglin Linda Liu; Grace Kuo; Nick Antipa; Kyrollos Yanny; Laura Waller
Fourier DiffuserScope: single-shot 3D Fourier light field microscopy with a diffuser Journal Article
In: Opt. Express, vol. 28, no. 20, pp. 28969–28986, 2020.
Abstract | Links | BibTeX | Tags: Light fields; Numerical simulation; Plenoptic imaging; Three dimensional imaging; Two photon polymerization; Wavefront encoding
@article{LindaLiu:20,
title = {Fourier DiffuserScope: single-shot 3D Fourier light field microscopy with a diffuser},
author = {Fanglin Linda Liu and Grace Kuo and Nick Antipa and Kyrollos Yanny and Laura Waller},
url = {http://www.opticsexpress.org/abstract.cfm?URI=oe-28-20-28969},
doi = {10.1364/OE.400876},
year = {2020},
date = {2020-09-01},
journal = {Opt. Express},
volume = {28},
number = {20},
pages = {28969--28986},
publisher = {OSA},
abstract = {Light field microscopy (LFM) uses a microlens array (MLA) near the sensor plane of a microscope to achieve single-shot 3D imaging of a sample without any moving parts. Unfortunately, the 3D capability of LFM comes with a significant loss of lateral resolution at the focal plane. Placing the MLA near the pupil plane of the microscope, instead of the image plane, can mitigate the artifacts and provide an efficient forward model, at the expense of field-of-view (FOV). Here, we demonstrate improved resolution across a large volume with Fourier DiffuserScope, which uses a diffuser in the pupil plane to encode 3D information, then computationally reconstructs the volume by solving a sparsity-constrained inverse problem. Our diffuser consists of randomly placed microlenses with varying focal lengths; the random positions provide a larger FOV compared to a conventional MLA, and the diverse focal lengths improve the axial depth range. To predict system performance based on diffuser parameters, we, for the first time, establish a theoretical framework and design guidelines, which are verified by numerical simulations, and then build an experimental system that achieves < 3 µm lateral and 4 µm axial resolution over a 1000 × 1000 × 280 µm3 volume. Our diffuser design outperforms the MLA used in LFM, providing more uniform resolution over a larger volume, both laterally and axially.},
keywords = {Light fields; Numerical simulation; Plenoptic imaging; Three dimensional imaging; Two photon polymerization; Wavefront encoding},
pubstate = {published},
tppubtype = {article}
}
Light field microscopy (LFM) uses a microlens array (MLA) near the sensor plane of a microscope to achieve single-shot 3D imaging of a sample without any moving parts. Unfortunately, the 3D capability of LFM comes with a significant loss of lateral resolution at the focal plane. Placing the MLA near the pupil plane of the microscope, instead of the image plane, can mitigate the artifacts and provide an efficient forward model, at the expense of field-of-view (FOV). Here, we demonstrate improved resolution across a large volume with Fourier DiffuserScope, which uses a diffuser in the pupil plane to encode 3D information, then computationally reconstructs the volume by solving a sparsity-constrained inverse problem. Our diffuser consists of randomly placed microlenses with varying focal lengths; the random positions provide a larger FOV compared to a conventional MLA, and the diverse focal lengths improve the axial depth range. To predict system performance based on diffuser parameters, we, for the first time, establish a theoretical framework and design guidelines, which are verified by numerical simulations, and then build an experimental system that achieves < 3 µm lateral and 4 µm axial resolution over a 1000 × 1000 × 280 µm3 volume. Our diffuser design outperforms the MLA used in LFM, providing more uniform resolution over a larger volume, both laterally and axially.
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 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.
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.
Nicholas Antipa
Lensless Computational Imaging using Random Optics PhD Thesis
2020.
Abstract | Links | BibTeX | Tags:
@phdthesis{Antipa:EECS-2020-175,
title = {Lensless Computational Imaging using Random Optics},
author = {Nicholas Antipa},
url = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2020/EECS-2020-175.html},
year = {2020},
date = {2020-08-01},
number = {UCB/EECS-2020-175},
institution = {EECS Department, University of California, Berkeley},
abstract = {Efficiently capturing high-dimensional optical signals, such as temporal dynamics, depth, perspective, or spectral content is a difficult imaging challenge. Because image sensors are inherently two-dimensional, direct sampling of the many dimensions that completely describe a scene presents a significant engineering challenge. Computational imaging is a design approach in which imaging hardware and digital signal processing algorithms are designed jointly to achieve performance not possible with partitioned design schemes. Within this paradigm, the sensing hardware is viewed as an encoder, coding the information of interest into measurements that can be captured with conventional sensors. Algorithms are then used to decode the information. In this dissertation, I explore the connection between optical imaging system design and compressed sensing, demonstrating that extra dimensions of optical signals (time, depth, and perspective) can be encoded into a single 2D measurement, then extracted using sparse recovery methods. The key to these capabilities is exploiting the inherent multiplexing properties of diffusers, pseudorandom free-form phase optics that scramble incident light. Contrary to their intended use, I show that certain classes of diffuser encode high-dimensional information about the incident light field into high-contrast, pseudorandom intensity patterns (caustics). Sparse recovery methods can then decode these patterns, recovering 3D images from snapshot 2D measurements. This transforms a diffuser into a computational imaging element for high-dimensional capture at video rates. Efficient physical models are introduced that reduce the computational burden for image recovery as compared to explicit matrix approaches (the computational cost remains high, however). Lastly, analysis and theory is developed that enables optimization of customized diffusers for miniaturized 3D fluorescence microscopy.},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
Efficiently capturing high-dimensional optical signals, such as temporal dynamics, depth, perspective, or spectral content is a difficult imaging challenge. Because image sensors are inherently two-dimensional, direct sampling of the many dimensions that completely describe a scene presents a significant engineering challenge. Computational imaging is a design approach in which imaging hardware and digital signal processing algorithms are designed jointly to achieve performance not possible with partitioned design schemes. Within this paradigm, the sensing hardware is viewed as an encoder, coding the information of interest into measurements that can be captured with conventional sensors. Algorithms are then used to decode the information. In this dissertation, I explore the connection between optical imaging system design and compressed sensing, demonstrating that extra dimensions of optical signals (time, depth, and perspective) can be encoded into a single 2D measurement, then extracted using sparse recovery methods. The key to these capabilities is exploiting the inherent multiplexing properties of diffusers, pseudorandom free-form phase optics that scramble incident light. Contrary to their intended use, I show that certain classes of diffuser encode high-dimensional information about the incident light field into high-contrast, pseudorandom intensity patterns (caustics). Sparse recovery methods can then decode these patterns, recovering 3D images from snapshot 2D measurements. This transforms a diffuser into a computational imaging element for high-dimensional capture at video rates. Efficient physical models are introduced that reduce the computational burden for image recovery as compared to explicit matrix approaches (the computational cost remains high, however). Lastly, analysis and theory is developed that enables optimization of customized diffusers for miniaturized 3D fluorescence microscopy.
Gautam Gunjala; Antoine Wojdyla; Stuart Sherwin; Aamod Shanker; Markus P. Benk; Kenneth A. Goldberg; Patrick P. Naulleau; Laura Waller
Extreme ultraviolet microscope characterization using photomask surface roughness Journal Article
In: Scientific Reports, vol. 10, no. 1, pp. 11673, 2020.
Links | BibTeX | Tags: aberrations, EUV, high-resolution, self-calibration
@article{gunjala2020extreme,
title = {Extreme ultraviolet microscope characterization using photomask surface roughness },
author = {Gautam Gunjala and Antoine Wojdyla and Stuart Sherwin and Aamod Shanker and Markus P. Benk and Kenneth A. Goldberg and Patrick P. Naulleau and Laura Waller },
url = {https://doi.org/10.1038/s41598-020-68588-w},
doi = {10.1038/s41598-020-68588-w},
year = {2020},
date = {2020-07-15},
journal = {Scientific Reports},
volume = {10},
number = {1},
pages = {11673},
keywords = {aberrations, EUV, high-resolution, self-calibration},
pubstate = {published},
tppubtype = {article}
}
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}
}
Michael Chen; David Ren; Hsiou-Yuan Liu; Shwetadwip Chowdhury; Laura Waller
Multi-layer Born multiple-scattering model for 3D phase microscopy Journal Article
In: Optica, vol. 7, no. 5, pp. 394–403, 2020.
Abstract | Links | BibTeX | Tags: 3D imaging, multiple-scattering, optical models, phase imaging
@article{Chen:20,
title = {Multi-layer Born multiple-scattering model for 3D phase microscopy},
author = {Michael Chen and David Ren and Hsiou-Yuan Liu and Shwetadwip Chowdhury and Laura Waller},
url = {http://www.osapublishing.org/optica/abstract.cfm?URI=optica-7-5-394},
doi = {10.1364/OPTICA.383030},
year = {2020},
date = {2020-05-01},
journal = {Optica},
volume = {7},
number = {5},
pages = {394--403},
publisher = {OSA},
abstract = {We propose an accurate and computationally efficient 3D scattering model, multi-layer Born (MLB), and use it to recover the 3D refractive index (RI) of thick biological samples. For inverse problems recovering the complex field of thick samples, weak scattering models (e.g., first Born) may fail or underestimate the RI, especially with a large index contrast. Multi-slice (MS) beam propagation methods model multiple scattering to provide more realistic reconstructions; however, MS does not properly account for highly oblique scattering, nor does it model backward scattering. Our proposed MLB model uses a first Born model at each of many slices, accurately capturing the oblique scattering effects and estimating the backward scattering process. When used in conjunction with an inverse solver, the model provides more accurate RI reconstructions for high-resolution phase tomography. Importantly, MLB retains a reasonable computation time that is critical for practical implementation with iterative inverse algorithms.},
keywords = {3D imaging, multiple-scattering, optical models, phase imaging},
pubstate = {published},
tppubtype = {article}
}
We propose an accurate and computationally efficient 3D scattering model, multi-layer Born (MLB), and use it to recover the 3D refractive index (RI) of thick biological samples. For inverse problems recovering the complex field of thick samples, weak scattering models (e.g., first Born) may fail or underestimate the RI, especially with a large index contrast. Multi-slice (MS) beam propagation methods model multiple scattering to provide more realistic reconstructions; however, MS does not properly account for highly oblique scattering, nor does it model backward scattering. Our proposed MLB model uses a first Born model at each of many slices, accurately capturing the oblique scattering effects and estimating the backward scattering process. When used in conjunction with an inverse solver, the model provides more accurate RI reconstructions for high-resolution phase tomography. Importantly, MLB retains a reasonable computation time that is critical for practical implementation with iterative inverse algorithms.
Grace Kuo; Fanglin Linda Liu; Irene Grossrubatscher; Ren Ng; Laura Waller
On-chip fluorescence microscopy with a random microlens diffuser Journal Article
In: Optics Express, vol. 28, no. 6, pp. 8384–8399, 2020.
Links | BibTeX | Tags: 3D imaging, diffuser, fluorescence imaging, lensless imaging, on-chip
@article{kuo2020chip,
title = {On-chip fluorescence microscopy with a random microlens diffuser},
author = { Grace Kuo and Fanglin Linda Liu and Irene Grossrubatscher and Ren Ng and Laura Waller},
url = {https://doi.org/10.1364/OE.382055},
doi = {10.1364/OE.382055},
year = {2020},
date = {2020-03-09},
journal = {Optics Express},
volume = {28},
number = {6},
pages = {8384--8399},
publisher = {Optical Society of America},
keywords = {3D imaging, diffuser, fluorescence imaging, lensless imaging, on-chip},
pubstate = {published},
tppubtype = {article}
}
Grace Kuo; Laura Waller; Ren Ng; Andrew Maimone
High Resolution éTendue Expansion for Holographic Displays Journal Article
In: ACM Trans. Graph., vol. 39, no. 4, 2020, ISSN: 0730-0301.
Abstract | Links | BibTeX | Tags: augmented reality, computational displays, computer generated holography, near-eye displays
@article{10.1145/3386569.3392414,
title = {High Resolution éTendue Expansion for Holographic Displays},
author = {Grace Kuo and Laura Waller and Ren Ng and Andrew Maimone},
url = {https://doi.org/10.1145/3386569.3392414},
doi = {10.1145/3386569.3392414},
issn = {0730-0301},
year = {2020},
date = {2020-01-01},
journal = {ACM Trans. Graph.},
volume = {39},
number = {4},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
abstract = {Holographic displays can create high quality 3D images while maintaining a small form factor suitable for head-mounted virtual and augmented reality systems. However, holographic displays have limited étendue based on the number of pixels in their spatial light modulators, creating a tradeoff between the eyebox size and the field-of-view. Scattering-based étendue expansion, in which coherent light is focused into an image after being scattered by a static mask, is a promising avenue to break this tradeoff. However, to date, this approach has been limited to very sparse content consisting of, for example, only tens of spots.In this work, we introduce new algorithms to scattering-based étendue expansion that support dense, photorealistic imagery at the native resolution of the spatial light modulator, offering up to a 20 dB improvement in peak signal to noise ratio over baseline methods. We propose spatial and frequency constraints to optimize performance for human perception, and performance is characterized both through simulation and a preliminary benchtop prototype. We further demonstrate the ability to generate content at multiple depths, and we provide a path for the miniaturization of our benchtop prototype into a sunglasses-like form factor.},
keywords = {augmented reality, computational displays, computer generated holography, near-eye displays},
pubstate = {published},
tppubtype = {article}
}
Holographic displays can create high quality 3D images while maintaining a small form factor suitable for head-mounted virtual and augmented reality systems. However, holographic displays have limited étendue based on the number of pixels in their spatial light modulators, creating a tradeoff between the eyebox size and the field-of-view. Scattering-based étendue expansion, in which coherent light is focused into an image after being scattered by a static mask, is a promising avenue to break this tradeoff. However, to date, this approach has been limited to very sparse content consisting of, for example, only tens of spots.In this work, we introduce new algorithms to scattering-based étendue expansion that support dense, photorealistic imagery at the native resolution of the spatial light modulator, offering up to a 20 dB improvement in peak signal to noise ratio over baseline methods. We propose spatial and frequency constraints to optimize performance for human perception, and performance is characterized both through simulation and a preliminary benchtop prototype. We further demonstrate the ability to generate content at multiple depths, and we provide a path for the miniaturization of our benchtop prototype into a sunglasses-like form factor.
David Ren; Colin Ophus; Michael Chen; Laura Waller
A multiple scattering algorithm for three dimensional phase contrast atomic electron tomography Journal Article
In: Ultramicroscopy, vol. 208, pp. 112860, 2020.
Links | BibTeX | Tags: 3D imaging, algorithms, multiple-scattering, phase imaging, TEM
@article{ren2020multiple,
title = {A multiple scattering algorithm for three dimensional phase contrast atomic electron tomography},
author = { David Ren and Colin Ophus and Michael Chen and Laura Waller},
url = {https://www.sciencedirect.com/science/article/pii/S030439911930052X},
year = {2020},
date = {2020-01-01},
journal = {Ultramicroscopy},
volume = {208},
pages = {112860},
publisher = {North-Holland},
keywords = {3D imaging, algorithms, multiple-scattering, phase imaging, TEM},
pubstate = {published},
tppubtype = {article}
}
Zachary F Phillips; Sarah Dean; Benjamin Recht; Laura Waller
High-throughput fluorescence microscopy using multi-frame motion deblurring Journal Article
In: Biomedical Optics Express, vol. 11, no. 1, pp. 281–300, 2019.
Links | BibTeX | Tags: deconvolution, fluorescence imaging, high-throughput, motion deblur
@article{phillips2020high,
title = {High-throughput fluorescence microscopy using multi-frame motion deblurring},
author = { Zachary F Phillips and Sarah Dean and Benjamin Recht and Laura Waller},
url = {https://doi.org/10.1364/BOE.11.000281},
doi = {10.1364/BOE.11.000281},
year = {2019},
date = {2019-12-16},
journal = {Biomedical Optics Express},
volume = {11},
number = {1},
pages = {281--300},
publisher = {Optical Society of America},
keywords = {deconvolution, fluorescence imaging, high-throughput, motion deblur},
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}
}
Shwetadwip Chowdhury; Michael Chen; Regina Eckert; David Ren; Fan Wu; Nicole A Repina; Laura Waller
High-resolution 3D refractive index microscopy of multiple-scattering samples from intensity images Journal Article
In: Optica, vol. 6, no. 9, pp. 1211–1219, 2019.
Links | BibTeX | Tags: 3D imaging, multiple-scattering, phase imaging
@article{chowdhury2019high,
title = {High-resolution 3D refractive index microscopy of multiple-scattering samples from intensity images},
author = { Shwetadwip Chowdhury and Michael Chen and Regina Eckert and David Ren and Fan Wu and Nicole A Repina and Laura Waller},
url = {https://doi.org/10.1364/OPTICA.6.001211},
doi = {10.1364/OPTICA.6.001211},
year = {2019},
date = {2019-09-16},
journal = {Optica},
volume = {6},
number = {9},
pages = {1211--1219},
publisher = {Optical Society of America},
keywords = {3D imaging, multiple-scattering, phase imaging},
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}
}
Colin Ophus; David Ren; Jihan Zhou; Hannah Devyldere; Michael Chen; Philipp M Pelz; Peter Ercius; Jianwei Miao; Mary C Scott; Laura Waller
3D Imaging Using HAADF-STEM and HRTEM Atomic Electron Tomography Journal Article
In: Microscopy and Microanalysis, vol. 25, no. S2, pp. 394–395, 2019.
Links | BibTeX | Tags: 3D imaging, multiple-scattering, phase imaging, TEM
@article{ophus20193d,
title = {3D Imaging Using HAADF-STEM and HRTEM Atomic Electron Tomography},
author = { Colin Ophus and David Ren and Jihan Zhou and Hannah Devyldere and Michael Chen and Philipp M Pelz and Peter Ercius and Jianwei Miao and Mary C Scott and Laura Waller},
url = {https://doi.org/10.1017/S1431927619002708},
doi = {10.1017/S1431927619002708},
year = {2019},
date = {2019-08-05},
journal = {Microscopy and Microanalysis},
volume = {25},
number = {S2},
pages = {394--395},
publisher = {Cambridge University Press},
keywords = {3D imaging, multiple-scattering, phase imaging, TEM},
pubstate = {published},
tppubtype = {article}
}
Li-Hao Yeh; Shwetadwip Chowdhury; Nicole A Repina; Laura Waller
Speckle-structured illumination for 3D phase and fluorescence computational microscopy Journal Article
In: Biomedical optics express, vol. 10, no. 7, pp. 3635–3653, 2019.
Links | BibTeX | Tags: 3D imaging, high-throughput, multiple-scattering, speckle
@article{yeh2019speckle,
title = {Speckle-structured illumination for 3D phase and fluorescence computational microscopy},
author = { Li-Hao Yeh and Shwetadwip Chowdhury and Nicole A Repina and Laura Waller},
url = {https://doi.org/10.1364/BOE.10.003635},
doi = {10.1364/BOE.10.003635},
year = {2019},
date = {2019-07-01},
journal = {Biomedical optics express},
volume = {10},
number = {7},
pages = {3635--3653},
publisher = {Optical Society of America},
keywords = {3D imaging, high-throughput, multiple-scattering, speckle},
pubstate = {published},
tppubtype = {article}
}
Henry Pinkard; Zachary F Phillips; Arman Babakhani; Daniel A Fletcher; Laura Waller
Deep learning for single-shot autofocus microscopy Journal Article
In: Optica, vol. 6, no. 6, pp. 794–797, 2019.
Links | BibTeX | Tags: algorithms, high-throughput, LED array
@article{pinkard2019deep,
title = {Deep learning for single-shot autofocus microscopy},
author = { Henry Pinkard and Zachary F Phillips and Arman Babakhani and Daniel A Fletcher and Laura Waller},
url = {https://www.osapublishing.org/optica/abstract.cfm?uri=optica-6-6-794},
year = {2019},
date = {2019-06-20},
journal = {Optica},
volume = {6},
number = {6},
pages = {794--797},
publisher = {Optical Society of America},
keywords = {algorithms, high-throughput, LED array},
pubstate = {published},
tppubtype = {article}
}
Tomas Aidukas; Regina Eckert; Andrew R Harvey; Laura Waller; Pavan C Konda
Low-cost, sub-micron resolution, wide-field computational microscopy using opensource hardware Journal Article
In: Scientific reports, vol. 9, no. 1, pp. 1–12, 2019.
Links | BibTeX | Tags: FPM, LED array, phase imaging, self-calibration
@article{aidukas2019low,
title = {Low-cost, sub-micron resolution, wide-field computational microscopy using opensource hardware},
author = { Tomas Aidukas and Regina Eckert and Andrew R Harvey and Laura Waller and Pavan C Konda},
url = {https://doi.org/10.1038/s41598-019-43845-9},
year = {2019},
date = {2019-05-15},
journal = {Scientific reports},
volume = {9},
number = {1},
pages = {1--12},
publisher = {Nature Publishing Group},
keywords = {FPM, LED array, phase imaging, self-calibration},
pubstate = {published},
tppubtype = {article}
}
Zachary F Phillips
Quantitative Microscopy using Coded Illumination PhD Thesis
University of California, Berkeley, 2019.
Links | BibTeX | Tags: algorithms, DPC, LED array, motion deblur, self-calibration
@phdthesis{phillips2019quant,
title = {Quantitative Microscopy using Coded Illumination},
author = {Zachary F Phillips},
url = {https://escholarship.org/uc/item/70d9j190
https://search.proquest.com/openview/ac36264c1a5b90ee3c8e4085759b0cc4/1?pq-origsite=gscholar&cbl=18750&diss=y},
year = {2019},
date = {2019-05-01},
school = {University of California, Berkeley},
keywords = {algorithms, DPC, LED array, motion deblur, self-calibration},
pubstate = {published},
tppubtype = {phdthesis}
}
Michael Chen
Coded Illumination for Multidimensional Quantitative Phase Imaging PhD Thesis
University of California, Berkeley, 2019.
Links | BibTeX | Tags: 3D imaging, coded illumination, DPC, phase imaging
@phdthesis{chen2019coded,
title = {Coded Illumination for Multidimensional Quantitative Phase Imaging},
author = {Michael Chen},
url = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2020/EECS-2020-37.html},
year = {2019},
date = {2019-05-01},
school = {University of California, Berkeley},
keywords = {3D imaging, coded illumination, DPC, phase imaging},
pubstate = {published},
tppubtype = {phdthesis}
}
Li-Hao Yeh
Computational fluorescence and phase super-resolution microscopy PhD Thesis
University of California, Berkeley, 2019.
Links | BibTeX | Tags: 3D imaging, fluorescence imaging, multiple-scattering, phase imaging, structured illumination
@phdthesis{yeh2019computationalb,
title = {Computational fluorescence and phase super-resolution microscopy},
author = {Li-Hao Yeh},
url = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2020/EECS-2020-36.html},
year = {2019},
date = {2019-05-01},
school = {University of California, Berkeley},
keywords = {3D imaging, fluorescence imaging, multiple-scattering, phase imaging, structured illumination},
pubstate = {published},
tppubtype = {phdthesis}
}
Li-Hao Yeh; Shwetadwip Chowdhury; Laura Waller
Computational structured illumination for high-content fluorescence and phase microscopy Journal Article
In: Biomedical optics express, vol. 10, no. 4, pp. 1978–1998, 2019.
Abstract | Links | BibTeX | Tags: diffuser, fluorescence imaging, high-throughput, phase imaging, structured illumination, super-resolution
@article{yeh2019computational,
title = {Computational structured illumination for high-content fluorescence and phase microscopy},
author = { Li-Hao Yeh and Shwetadwip Chowdhury and Laura Waller},
url = {https://www.osapublishing.org/boe/abstract.cfm?uri=boe-10-4-1978
https://doi.org/10.1364/BOE.10.001978},
doi = {10.1364/BOE.10.001978},
year = {2019},
date = {2019-04-01},
journal = {Biomedical optics express},
volume = {10},
number = {4},
pages = {1978--1998},
publisher = {Optical Society of America},
abstract = {High-content biological microscopy targets high-resolution imaging across large fields-of-view (FOVs). Recent works have demonstrated that computational imaging can provide efficient solutions for high-content microscopy. Here, we use speckle structured illumination microscopy (SIM) as a robust and cost-effective solution for high-content fluorescence microscopy with simultaneous high-content quantitative phase (QP). This multi-modal compatibility is essential for studies requiring cross-correlative biological analysis. Our method uses laterally-translated Scotch tape to generate high-resolution speckle illumination patterns across a large FOV. Custom optimization algorithms then jointly reconstruct the sample’s super-resolution fluorescent (incoherent) and QP (coherent) distributions, while digitally correcting for system imperfections such as unknown speckle illumination patterns, system aberrations and pattern translations. Beyond previous linear SIM works, we achieve resolution gains of 4× the objective’s diffraction-limited native resolution, resulting in 700 nm fluorescence and 1.2 μm QP resolution, across a FOV of 2×2.7 mm 2, giving a space-bandwidth product (SBP) of 60 megapixels.},
keywords = {diffuser, fluorescence imaging, high-throughput, phase imaging, structured illumination, super-resolution},
pubstate = {published},
tppubtype = {article}
}
High-content biological microscopy targets high-resolution imaging across large fields-of-view (FOVs). Recent works have demonstrated that computational imaging can provide efficient solutions for high-content microscopy. Here, we use speckle structured illumination microscopy (SIM) as a robust and cost-effective solution for high-content fluorescence microscopy with simultaneous high-content quantitative phase (QP). This multi-modal compatibility is essential for studies requiring cross-correlative biological analysis. Our method uses laterally-translated Scotch tape to generate high-resolution speckle illumination patterns across a large FOV. Custom optimization algorithms then jointly reconstruct the sample’s super-resolution fluorescent (incoherent) and QP (coherent) distributions, while digitally correcting for system imperfections such as unknown speckle illumination patterns, system aberrations and pattern translations. Beyond previous linear SIM works, we achieve resolution gains of 4× the objective’s diffraction-limited native resolution, resulting in 700 nm fluorescence and 1.2 μm QP resolution, across a FOV of 2×2.7 mm 2, giving a space-bandwidth product (SBP) of 60 megapixels.
Laura Waller; Hillel Adesnik; Nicolas C Pégard
Three-dimensional scanless holographic optogenetics with temporal focusing Patent
2019, (US Patent App. 16/255,557).
Links | BibTeX | Tags: 3D imaging, fluorescence imaging, neural imaging
@patent{waller2019three,
title = {Three-dimensional scanless holographic optogenetics with temporal focusing},
author = { Laura Waller and Hillel Adesnik and Nicolas C Pégard},
url = {https://patents.google.com/patent/US20190227490A1/en?},
year = {2019},
date = {2019-01-23},
note = {US Patent App. 16/255,557},
keywords = {3D imaging, fluorescence imaging, neural imaging},
pubstate = {published},
tppubtype = {patent}
}
Aamod Shanker; Antoine Wojdyla; Laura Waller; Andrew R. Neureuther
Differential methods in phase imaging for optical lithography PhD Thesis
PhD thesis, University of California, Berkeley (May 2018), 2018.
Links | BibTeX | Tags: EUV, lithography, phase from defocus, phase imaging, TIE
@phdthesis{shanker2018differential,
title = {Differential methods in phase imaging for optical lithography},
author = { Aamod Shanker and Antoine Wojdyla and Laura Waller and Andrew R. Neureuther},
url = {https://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-160.html
http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-160.pdf},
year = {2018},
date = {2018-12-01},
school = {PhD thesis, University of California, Berkeley (May 2018)},
keywords = {EUV, lithography, phase from defocus, phase imaging, TIE},
pubstate = {published},
tppubtype = {phdthesis}
}