A novel class of imaging systems that combines diffractive optics with 1D line sensing is disclosed. When light passes through a diffraction grating or prism, it disperses as a function of wavelength. This property is exploited to recover 2D and 3D positions from line images. A detailed image formation model and a learning-based algorithm for 2D position estimation are disclosed. The disclosure includes several extensions of the imaging system to improve the accuracy of the 2D position estimates and to expand the effective field-of-view. The invention is useful for fast passive imaging of sparse light sources, such as streetlamps, headlights at night and LED-based motion capture, and structured light 3D scanning with line illumination and line sensing.

A microscopy system includes a planar array of micro-cameras with at least three micro-cameras of the planar array of micro-cameras each capturing a unique angular distribution of light reflected from a corresponding portion of a target area. The corresponding portions of the target area for the at least three micro-cameras contain an overlapping area of the target area. The microscopy system further includes a primary lens disposed in a path of the light between the planar array of micro-cameras and the target area. This microscopy system is capable of producing 3D imaging and/or video imaging of the target area. In some cases, the microscopy system is configured to generate a 3D image from the captured unique angular distribution of light reflected from the corresponding portions of the target area of the at least three micro-cameras of the planar array of micro-cameras.

A microscopy system includes a planar array of micro-cameras with at least three micro-cameras of the planar array of micro-cameras each capturing a unique angular distribution of light reflected from a corresponding portion of a target area. The corresponding portions of the target area for the at least three micro-cameras contain an overlapping area of the target area. The microscopy system further includes a primary lens disposed in a path of the light between the planar array of micro-cameras and the target area. This microscopy system is capable of producing 3D imaging and/or video imaging of the target area. In some cases, the microscopy system is configured to generate a 3D image from the captured unique angular distribution of light reflected from the corresponding portions of the target area of the at least three micro-cameras of the planar array of micro-cameras.

An image processing apparatus includes at least one memory configured to store instructions, and at least one processor configured to execute the instructions to acquire a captured image obtained using an optical system, and information on an imaging condition corresponding to the captured image, and perform deteriorating processing for the captured image based on an F-number of the optical system, the F-number being included in the information on the imaging condition. A first deterioration amount in the deteriorating processing in a case where the F-number is a first F-number is smaller than a second deterioration amount in the deteriorating processing in a case where the F-number is a second F-number larger than the first F-number.

A method and a system for calculating a focusing parameter of a binocular stereo camera, and an intelligent terminal are provided. The method includes: calculating a lower limit value and an upper limit value of a distance measurement range; acquiring a depth-of-field range in accordance with the lower limit value and the upper limit value; calculating a maximum value and a minimum value of an image distance gain corresponding to a real object distance within the depth-of-field range, and calculating an optimal image distance gain in accordance with the maximum value and the minimum value; acquiring an optimal object distance of a focused target in accordance with the optimal image distance gain; and calculating an optimal size of the focused target in accordance with the optimal object distance.

A method and a system for imaging a dynamic scene, by time-spectrum mapping when a single chirped pulse probes the dynamic scene, storing temporal information at different wavelengths, spectral shearing, spatial encoding and reverse spectral shearing; spatiotemporal integration; and image reconstruction from a resulting captured snapshot, using a laser source configured to emit a linearly chirped laser probe pulse; an imaging unit; a shearing and reversing shearing unit; an encoder; a detector; and a computer; wherein the imaging unit is configured to record the linearly chirped laser probe pulse transmitted by the dynamic scene in a snapshot; the shearing and reversing shearing unit is configured to spectrally shears the linearly chirped laser pulses received from the imaging unit to the encoder, the detector records a compressed snapshot of a temporal information of the dynamic scene read out by the probe pulse; and the computer processes the snapshot and yields a(x,y,t) datacube of the dynamic scene.

A method and a system for imaging a dynamic scene, by time-spectrum mapping when a single chirped pulse probes the dynamic scene, storing temporal information at different wavelengths, spectral shearing, spatial encoding and reverse spectral shearing; spatiotemporal integration; and image reconstruction from a resulting captured snapshot, using a laser source configured to emit a linearly chirped laser probe pulse; an imaging unit; a shearing and reversing shearing unit; an encoder; a detector; and a computer; wherein the imaging unit is configured to record the linearly chirped laser probe pulse transmitted by the dynamic scene in a snapshot; the shearing and reversing shearing unit is configured to spectrally shears the linearly chirped laser pulses received from the imaging unit to the encoder, the detector records a compressed snapshot of a temporal information of the dynamic scene read out by the probe pulse; and the computer processes the snapshot and yields a(x,y,t) datacube of the dynamic scene.

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.

Provided is a learning device that acquires computational imaging information of a computational imaging camera that captures an image with blurring; acquires a normal image captured by a normal camera that captures an image without blurring or an image with blurring smaller than that of the computational imaging camera, and a correct answer label assigned to the normal image; generates an image with blurring based on the computational imaging information and the normal image; and performs machine learning using the image with blurring and the correct answer label to create an image identification model for identifying an image captured by the computational imaging camera.

Provided is a learning device that acquires computational imaging information of a computational imaging camera that captures an image with blurring; acquires a normal image captured by a normal camera that captures an image without blurring or an image with blurring smaller than that of the computational imaging camera, and a correct answer label assigned to the normal image; generates an image with blurring based on the computational imaging information and the normal image; and performs machine learning using the image with blurring and the correct answer label to create an image identification model for identifying an image captured by the computational imaging camera.