Journal papers
Kristina Monakhova; Kyrollos Yanny; Neerja Aggarwal; Laura Waller
Spectral DiffuserCam: lensless snapshot hyperspectral imaging with a spectral filter array Journal Article
In: Optica, vol. 7, no. 10, pp. 1298–1307, 2020.
Abstract | Links | BibTeX | Tags: algorithm, compressed sensing, diffuser, Hyperspectral imaging; Imaging systems; Optical components; Optical design; Spectral imaging; Systems design, Image reconstruction, Image sensors, Inverse problems, Sensors
@article{Monakhova:20b,
title = {Spectral DiffuserCam: lensless snapshot hyperspectral imaging with a spectral filter array},
author = {Kristina Monakhova and Kyrollos Yanny and Neerja Aggarwal and Laura Waller},
url = {http://www.osapublishing.org/optica/abstract.cfm?URI=optica-7-10-1298},
doi = {10.1364/OPTICA.397214},
year = {2020},
date = {2020-10-01},
journal = {Optica},
volume = {7},
number = {10},
pages = {1298--1307},
publisher = {OSA},
abstract = {Hyperspectral imaging is useful for applications ranging from medical diagnostics to agricultural crop monitoring; however, traditional scanning hyperspectral imagers are prohibitively slow and expensive for widespread adoption. Snapshot techniques exist but are often confined to bulky benchtop setups or have low spatio-spectral resolution. In this paper, we propose a novel, compact, and inexpensive computational camera for snapshot hyperspectral imaging. Our system consists of a tiled spectral filter array placed directly on the image sensor and a diffuser placed close to the sensor. Each point in the world maps to a unique pseudorandom pattern on the spectral filter array, which encodes multiplexed spatio-spectral information. By solving a sparsity-constrained inverse problem, we recover the hyperspectral volume with sub-super-pixel resolution. Our hyperspectral imaging framework is flexible and can be designed with contiguous or non-contiguous spectral filters that can be chosen for a given application. We provide theory for system design, demonstrate a prototype device, and present experimental results with high spatio-spectral resolution.},
keywords = {algorithm, compressed sensing, diffuser, Hyperspectral imaging; Imaging systems; Optical components; Optical design; Spectral imaging; Systems design, Image reconstruction, Image sensors, Inverse problems, Sensors},
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}
}
Michael Kellman; Michael Chen; Zachary F Phillips; Michael Lustig; Laura Waller
Motion-resolved quantitative phase imaging Journal Article
In: Biomedical optics express, vol. 9, no. 11, pp. 5456–5466, 2018.
Links | BibTeX | Tags: algorithm, high throughput, phase imaging
@article{kellman2018motionb,
title = {Motion-resolved quantitative phase imaging},
author = { Michael Kellman and Michael Chen and Zachary F Phillips and Michael Lustig and Laura Waller},
url = {https://doi.org/10.1364/BOE.9.005456},
doi = {10.1364/BOE.9.005456},
year = {2018},
date = {2018-10-15},
journal = {Biomedical optics express},
volume = {9},
number = {11},
pages = {5456--5466},
publisher = {Optical Society of America},
keywords = {algorithm, high throughput, phase imaging},
pubstate = {published},
tppubtype = {article}
}