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Full-Text Articles in Physics
Compressive Direct Imaging Of A Billion-Dimensional Optical Phase Space, Samuel H. Knarr, Daniel J. Lum, James Schneeloch, John C. Howell
Compressive Direct Imaging Of A Billion-Dimensional Optical Phase Space, Samuel H. Knarr, Daniel J. Lum, James Schneeloch, John C. Howell
Mathematics, Physics, and Computer Science Faculty Articles and Research
Optical phase spaces represent fields of any spatial coherence and are typically measured through phase-retrieval methods involving a computational inversion, optical interference, or a resolution-limiting lenslet array. Recently, a weak-values technique demonstrated that a beam's Dirac phase space is proportional to the measurable complex weak value, regardless of coherence. These direct measurements require raster scanning through all position-polarization couplings, limiting their dimensionality to less than 100 000 [C. Bamber and J. S. Lundeen, Phys. Rev. Lett. 112, 070405 (2014)]. We circumvent these limitations using compressive sensing, a numerical protocol that allows us to undersample, yet efficiently measure, high-dimensional phase spaces. …
Short-Wave Infrared Compressive Imaging Of Single Photons, Thomas Gerrits, Daniel J. Lum, Varun B. Verma, John C. Howell, Richard P. Mirin, Sae Woo Nam
Short-Wave Infrared Compressive Imaging Of Single Photons, Thomas Gerrits, Daniel J. Lum, Varun B. Verma, John C. Howell, Richard P. Mirin, Sae Woo Nam
Mathematics, Physics, and Computer Science Faculty Articles and Research
We present a short-wave infrared (SWIR) single photon camera based on a single superconducting nanowire single photon detector (SNSPD) and compressive imaging. We show SWIR single photon imaging at a megapixel resolution with a low signal-to-background ratio around 0.6, show SWIR video acquisition at 20 frames per second and 64x64 pixel video resolution, and demonstrate sub-nanosecond resolution time-of-flight imaging. All scenes were sampled by detecting only a small number of photons for each compressive sampling matrix. In principle, our technique can be used for imaging faint objects in the mid-IR regime.
Frequency-Modulated Continuous-Wave Lidar Compressive Depth-Mapping, Daniel J. Lum, Samuel H. Knarr, John C. Howell
Frequency-Modulated Continuous-Wave Lidar Compressive Depth-Mapping, Daniel J. Lum, Samuel H. Knarr, John C. Howell
Mathematics, Physics, and Computer Science Faculty Articles and Research
We present an inexpensive architecture for converting a frequency-modulated continuous-wave LiDAR system into a compressive-sensing based depth-mapping camera. Instead of raster scanning to obtain depth-maps, compressive sensing is used to significantly reduce the number of measurements. Ideally, our approach requires two difference detectors. Due to the large flux entering the detectors, the signal amplification from heterodyne detection, and the effects of background subtraction from compressive sensing, the system can obtain higher signal-to-noise ratios over detector-array based schemes while scanning a scene faster than is possible through raster-scanning. Moreover, by efficiently storing only 2m data points from m < n measurements of an n pixel scene, we can easily extract depths by solving only two linear equations with efficient convex-optimization methods.
Frequency Modulated Continuous Wave Compressive Depth Mapping, Daniel J. Lum, Samuel H. Knarr, John C. Howell
Frequency Modulated Continuous Wave Compressive Depth Mapping, Daniel J. Lum, Samuel H. Knarr, John C. Howell
Mathematics, Physics, and Computer Science Faculty Articles and Research
We present an inexpensive architecture for converting a frequency-modulated continuous-wave LiDAR system into a compressive-sensing based depth-mapping camera. Instead of raster scanning to obtain depth-maps, compressive sensing is used to significantly reduce the number of measurements. Ideally, our approach requires two difference detectors. Due to the large flux entering the detectors, the signal amplification from heterodyne detection, and the effects of background subtraction from compressive sensing, the system can obtain higher signal-to-noise ratios over detector-array based schemes while scanning a scene faster than is possible through raster-scanning. Moreover, by efficiently storing only 2m data points from m < n measurements of an n pixel scene, we can easily extract depths by solving only two linear equations with efficient convex-optimization methods.