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Compressive focal plane arrays (FPA) enable cost-effective high-resolution (HR) imaging by acquisition of several multiplexed measurements on a low-resolution (LR) sensor. Multiplexed encoding of the visual scene is typically performed via electronically controllable spatial light modulators (SLM). An HR image is then reconstructed from the encoded measurements by solving an inverse problem that involves the forward model of the imaging system. To capture system non-idealities such as optical aberrations, a mainstream approach is to conduct an offline calibration scan to measure the system response for a point source at each spatial location on the imaging grid. However, it is challenging to run calibration scans when using structured SLMs as they cannot encode individual grid locations. In this study, we propose a novel compressive FPA system based on online deep-learning calibration of multiplexed LR measurements (CalibFPA). We introduce a piezo-stage that locomotes a pre-printed fixed coded aperture. A deep neural network is then leveraged to correct for the influences of system non-idealities in multiplexed measurements without the need for offline calibration scans. Finally, a deep plug-and-play algorithm is used to reconstruct images from corrected measurements. On simulated and experimental datasets, we demonstrate that CalibFPA outperforms state-of-the-art compressive FPA methods. We also report analyses to validate the design elements in CalibFPA and assess computational complexity.

Objective: to compare the effectiveness of different mask types in limiting the dispersal of coughed air. Method: the Schlieren method with a single curved mirror was used in this study. Coughed air has a slightly higher temperature than ambient air, which generates a refractive index gradient. A curved mirror with a radius of curvature of 10 m and a diameter of 60 cm was used. The spread of the cough wavefront was investigated among five subjects wearing: (1) no mask; (2) a single surgical mask; (3) a double surgical mask; (4) a cloth mask; (5) a valveless N95 mask; and (6) a valved N95 mask. Results: all mask types reduced the size of the contaminated region significantly. The percentage reduction in the cross-sectional area of the contaminated region for the same mask types on different subjects revealed by normalized data suggests that the fit of a mask plays an important role. Conclusions: no significant difference in the spread of coughed air was found between the use of a single surgical mask or a double surgical mask. Cloth masks may be effective, depending on the quality of the cloth. Valved N95 masks exclusively protect the user. The fit of a mask is an important factor to minimize the contaminated region.

Objective: to compare the effectiveness of different mask types in limiting the dispersal of coughed air. Method: the Schlieren method with a single curved mirror was used in this study. Coughed air has a slightly higher temperature than ambient air, which generates a refractive index gradient. A curved mirror with a radius of curvature of 10 m and a diameter of 60 cm was used. The spread of the cough wavefront was investigated among five subjects wearing: (1) no mask; (2) a single surgical mask; (3) a double surgical mask; (4) a cloth mask; (5) a valveless N95 mask; and (6) a valved N95 mask. Results: all mask types reduced the size of the contaminated region significantly. The percentage reduction in the cross-sectional area of the contaminated region for the same mask types on different subjects revealed by normalized data suggests that the fit of a mask plays an important role. Conclusions: no significant difference in the spread of coughed air was found between the use of a single surgical mask or a double surgical mask. Cloth masks may be effective, depending on the quality of the cloth. Valved N95 masks exclusively protect the user. The fit of a mask is an important factor to minimize the contaminated region., 2232 International Fellowship for Outstanding Researchers Program; Scientific and Technological Research Council of Turkey (TÜBİTAK)