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}
}
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.
Nick Antipa; Grace Kuo; Reinhard Heckel; Ben Mildenhall; Emrah Bostan; Ren Ng; Laura Waller
DiffuserCam: Lensless single-exposure 3D imaging Journal Article
In: Optica, vol. 5, no. 1, pp. 1–9, 2018.
Links | BibTeX | Tags: compressed sensing, diffuser, lensless imaging
@article{antipa2018diffusercam,
title = {DiffuserCam: Lensless single-exposure 3D imaging},
author = { Nick Antipa and Grace Kuo and Reinhard Heckel and Ben Mildenhall and Emrah Bostan and Ren Ng and Laura Waller},
url = {https://doi.org/10.1364/OPTICA.5.000001},
doi = {10.1364/OPTICA.5.000001},
year = {2018},
date = {2018-01-20},
journal = {Optica},
volume = {5},
number = {1},
pages = {1--9},
publisher = {Optical Society of America},
keywords = {compressed sensing, diffuser, lensless imaging},
pubstate = {published},
tppubtype = {article}
}