Abstract: Real time, low-light images, for example, obtained from the fluorescent marker for identifying tumors during surgery, are combined to improve the signal-to-noise ratio using a motion signal derived from corresponding high-light images, for example, taken with a second camera at interleaved intervals of higher illumination.

Abstract: In accordance with some embodiments, various examples of systems, methods, and media for single photon depth imaging with improved efficiency using learned compressive representations are provided. For example, systems and methods herein may detect a photon arrival based on a signal from a detector at a given position and time bin. A compressed histogram comprising K stored values, representing bins of the compressed histogram based on K values in a code word calculated based on the time bin and position and K coding tensors. Such systems and methods may be utilized for, among other uses, determining depth in a scene being imaged by the detector.

Abstract: The present application is directed to a system and method for creating a precision three dimensional biomap and/or digital twin representation. A representative method includes receiving, by a computing system, medical imaging data for a patient and organizing, by the computing system, the medical imaging data into hierarchical virtual space data. The method further includes creating, by the computing system, a three dimensional biomap of the patient based on the hierarchical virtual space data.

Abstract: In accordance with some embodiments, systems, methods and media for single photon depth imaging with improved precision in ambient light conditions are provided. In some embodiments, the system comprises: a light source; a single photon detector; an attenuation element configured to provide a variable intensity attenuation factor; and a processor programmed to: (a)-determine an ambient light intensity associated with a scene point; (b)-select an attenuation factor based on the ambient light intensity; (c)-estimate a depth of the scene point based on a multiplicity of photon arrival times determined using the detector during a period of time during which light incident on the detector is attenuated by the selected attenuation factor and during which the light source is configured to periodically emit a pulse of light toward the scene point; (d)-repeat (a)-(c) for each of a multiplicity of scene points.

Abstract: In accordance with some embodiments, systems, methods, and media for high dynamic range imaging using single-photon and conventional image sensor data are provided. In some embodiments, the system comprises: first detectors configured to detect a level of photons proportional to incident photon flux; second detectors configured to detect arrival of individual photons; a processor programmed to: receive, from the first detectors, first values indicative of photon flux from a scene with a first resolution; receive, from the second detectors, second values indicative of photon flux from the scene with a lower resolution; provide a first encoder of a trained machine learning model first flux values based on the first values, provide the second encoder of the model second flux values; receive, as output, values indicative of photon flux from the scene; and generate a high dynamic range image based on the third plurality of values.

Abstract: In accordance with some embodiments, systems, methods, and media for single photon depth imaging with improved efficiency using compressive histograms are provided. In some embodiments, the system comprises: a light source; a detector configured to detect arrival of individual photons; a processor programmed to: detect a photon arrival; determine a time bin i of the photon arrival in a range from 1 to N a total number of time bins; update a compressed histogram comprising K stored values representing bins of the compressed histogram based on K values in a code word represented by an ith column of a coding matrix C having dimension K×N, with each column different than each other column, and each column corresponds to a single time bin i; and estimate a depth value based on the K values.

Abstract: Real time, low-light images, for example, obtained from the fluorescent marker for identifying tumors during surgery, are combined to improve the signal-to-noise ratio using a motion signal derived from corresponding high-light images, for example, taken with a second camera at interleaved intervals of higher illumination.

Abstract: In accordance with some embodiments, systems, methods, and media for high dynamic range imaging using single-photon and conventional image sensor data are provided. In some embodiments, the system comprises: first detectors configured to detect a level of photons proportional to incident photon flux; second detectors configured to detect arrival of individual photons; a processor programmed to: receive, from the first detectors, first values indicative of photon flux from a scene with a first resolution; receive, from the second detectors, second values indicative of photon flux from the scene with a lower resolution; provide a first encoder of a trained machine learning model first flux values based on the first values, provide the second encoder of the model second flux values; receive, as output, values indicative of photon flux from the scene; and generate a high dynamic range image based on the third plurality of values.

Abstract: In accordance with some embodiments, systems, methods, and media for high dynamic range imaging using single-photon and conventional image sensor data are provided. In some embodiments, the system comprises: first detectors configured to detect a level of photons proportional to incident photon flux; second detectors configured to detect arrival of individual photons; a processor programmed to: receive, from the first detectors, first values indicative of photon flux from a scene with a first resolution; receive, from the second detectors, second values indicative of photon flux from the scene with a lower resolution; provide a first encoder of a trained machine learning model first flux values based on the first values, provide the second encoder of the model second flux values; receive, as output, values indicative of photon flux from the scene; and generate a high dynamic range image based on the third plurality of values.

Abstract: In accordance with some embodiments, systems, methods, and media for motion adaptive imaging using single-photon image sensor data are provided. In some embodiments, the system comprises: an image sensor comprising single-photon detectors in an array; a processor programmed to: receive a sequence of photon frames, each comprising pixels having a value indicative of whether a photon was received during a frame period, each of the pixels corresponds to a pixel location; identify, for each of the pixel locations, changepoints, each indicative of a change in scene brightness; identify a photon frame in the sequence at which at least a threshold change in brightness has occurred based on the changepoints associated with each of the plurality of pixel locations; and generate a series of changepoint frames, wherein each changepoint frame is based on estimated brightness associated with each pixel location at a point in the sequence of photon frames.

Abstract: In accordance with some embodiments, systems, methods and media for single photon depth imaging with improved precision in ambient light conditions are provided. In some embodiments, the system comprises: a light source; a single photon detector; an attenuation element configured to provide a variable intensity attenuation factor; and a processor programmed to: (a)-determine an ambient light intensity associated with a scene point; (b)-select an attenuation factor based on the ambient light intensity; (c)-estimate a depth of the scene point based on a multiplicity of photon arrival times determined using the detector during a period of time during which light incident on the detector is attenuated by the selected attenuation factor and during which the light source is configured to periodically emit a pulse of light toward the scene point; (d)-repeat (a)-(c) for each of a multiplicity of scene points.

Abstract: In accordance with some embodiments, systems, methods and media for encoding and decoding signals used in time-of-flight imaging are provided. In some embodiments, a method for estimating the depth of a scene is provided, comprising: causing a light source to emit modulated light toward the scene based on a modulation function; causing the image sensor to generate a first value based on the modulated light and a first demodulation function of K modulation functions; causing the image sensor to generate a second value; causing the image sensor to generate a third value; and determining a depth estimate for the portion of the scene based on the first value, the second value, the third value, and three correlation functions each including at least one half of a trapezoid wave.

Abstract: In accordance with some embodiments, systems, methods and media for encoding and decoding signals used in time-of-flight imaging are provided. In some embodiments, a method for estimating the depth of a scene is provided, comprising: causing a light source to emit modulated light toward the scene based on a modulation function; causing the image sensor to generate a first value based on the modulated light and a first demodulation function of K modulation functions, including at least one trapezoid wave; causing the image sensor to generate a second value; causing the image sensor to generate a third value; and determining a depth estimate for the portion of the scene based on the first value, the second value, and the third value.

Abstract: In accordance with some embodiments, systems, methods and media for high dynamic range imaging using dead-time-limited single photon detectors are provided. In some embodiments, a system for high dynamic range imaging is provided, comprising: an image sensor comprising: a pixels comprising: a single photon detector having dead time ?d; and a counter coupled to an output of the single photon detector, wherein the counter is configured to increment in response to a signal indicative of detection of a photon output by the single photon detector; and a processor that is programmed to: read out a value stored by the counter after an exposure time has elapsed; and calculate an intensity for the pixel based on the value and the dead time ?d.

Abstract: In accordance with some embodiments, systems, methods and media for high dynamic range imaging using dead-time-limited single photon detectors are provided. In some embodiments, a system for high dynamic range imaging is provided, comprising: an image sensor comprising: a pixels comprising: a single photon detector having dead time ?d; and a counter coupled to an output of the single photon detector, wherein the counter is configured to increment in response to a signal indicative of detection of a photon output by the single photon detector; and a processor that is programmed to: read out a value stored by the counter after an exposure time has elapsed; and calculate an intensity for the pixel based on the value and the dead time ?d.

Abstract: In accordance with some embodiments, systems, methods and media for encoding and decoding signals used in time-of-flight imaging are provided. In some embodiments, a method for estimating the depth of a scene is provided, comprising: causing a light source to emit modulated light toward the scene based on a modulation function; causing the image sensor to generate a first value based on the modulated light and a first demodulation function of K modulation functions; causing the image sensor to generate a second value; causing the image sensor to generate a third value; and determining a depth estimate for the portion of the scene based on the first value, the second value, the third value, and three correlation functions each including at least one half of a trapezoid wave.

Abstract: In accordance with some embodiments, systems, methods and media for encoding and decoding signals used in time-of-flight imaging are provided. In some embodiments, a method for estimating the depth of a scene is provided, comprising: causing a light source to emit modulated light toward the scene based on a modulation function; causing the image sensor to generate a first value based on the modulated light and a first demodulation function of K modulation functions, including at least one trapezoid wave; causing the image sensor to generate a second value; causing the image sensor to generate a third value; and determining a depth estimate for the portion of the scene based on the first value, the second value, and the third value.

Abstract: Fluorescent markers used to identify a tissue may be imaged in a bright environment by synchronizing the imaging process with rapidly switched ambient lighting so that imaging occurs in phase with a switching off of the ambient lighting. In this way, valuable fluorescent imaging may be performed in an environment that appears to be brightly illuminated, for example in the area of a surgical suite.

Abstract: An active imaging system, which includes a light source and light sensor, generates structured illumination. The light sensor captures transient light response data regarding reflections of light emitted by the light source. The transient light response data is wavelength-resolved. One or more processors process the transient light response data and data regarding the structured illumination to calculate a reflectance spectra map of an occluded surface. The processors also compute a 3D geometry of the occluded surface.

Abstract: An active imaging system, which includes a light source and light sensor, generates structured illumination. The light sensor captures transient light response data regarding reflections of light emitted by the light source. The transient light response data is wavelength-resolved. One or more processors process the transient light response data and data regarding the structured illumination to calculate a reflectance spectra map of an occluded surface. The processors also compute a 3D geometry of the occluded surface.