A method comprises obtaining input data comprising a hologram of a 3-dimensional (3D) particle field, a depth map of the 3D particle field, and a maximum phase projection of the 3D particle field. The method also comprises applying a U-net convolutional neural network (CNN) to the input data to generate output data. Encoder blocks have residual connections between a first layer and a second layer that skips over a convolution layer of the encoder block. Decoder blocks have residual connections between a first layer and a second layer that skips over a convolution layer of the decoder block. The output data includes a channel in which pixel intensity corresponds to relative depth of particles in the 3D particle field and an output image indicating locations of centroids of the particles in the 3D particle field.

A device for detecting at least one object present in a sample, the device including a light source to emit at least one incident wave at a wavelength λ, a detection volume intended to receive the object, and to receive at least one incident wave, an image sensor positioned to receive at least one scattered light wave obtained by diffraction of the incident wave on the object and a reference wave from the source and not diffracted on the object and to generate a holographic image, and a computer data processing device to digitally reconstruct the object based at least on the holographic image and the wavelength λ. The device also comprises a support comprising patterns organized to form at least one diffraction grating, the grating being periodic and having a pitch P, such that λ/2≤P≤2λ.

A method for three-dimensional imaging of a sample (302) comprises: receiving (102) interference patterns (208) acquired using light-detecting elements (212), wherein each interference pattern (208) is formed by scattered light from the sample (302) and non-scattered light from a light source (206; 306), wherein the interference patterns (208) are acquired using different angles between the sample (302) and the light source (206; 306); performing digital holographic reconstruction applying an iterative algorithm to change a three-dimensional scattering potential of the sample (302) to improve a difference between the received interference patterns (208) and predicted interference patterns based on the three-dimensional scattering potential; wherein the iterative algorithm reduces a sum of a data fidelity term and a non-differentiable regularization term and wherein the iterative algorithm includes a forward-backward splitting method alternating between forward gradient descent (108) on the data fidelity term and backward gradient descent (110) on the regularization term.

To obtain a more accurate image by improving a utilization efficiency of light energy while at the same time suppressing with a simpler method distortion that may occur in an inline hologram when a plurality of lights having different wavelengths are used, an observation device (1) according to the present disclosure includes a light source part (11) in which a plurality of light emitting diodes (101) having different light emission wavelengths with a length of each light emission point being smaller than 100λ (λ: light emission wavelength) are arranged such that a separation distance between the adjacent light emitting diodes is equal to or smaller than 100λ (λ: light emission wavelength); and an image sensor (13) installed so as to be opposed to the light source part with respect to an observation target object.

An in-line holographic microscope can be used to analyze a video stream to track individual colloidal particles' three-dimensional motions. The system and method can provide real time nanometer resolution, and simultaneously measure particle sizes and refractive indexes. An assay using the holographic microscope for holographic particle characterization directly detect viruses, antibodies and related targets binding to the surfaces of specifically functionalized micrometer-scale colloidal probe beads. The system detects binding of targets by directly measuring associated changes in the bead's diameter without the need for downstream labeling and analysis.

Holographic Video Microscopy analysis of non-spherical particles is disclosed herein. Properties of the particles are determined by application of light scattering theory to holography data. Effective sphere theory is applied to provide information regarding the reflective index of a sphere that includes a target particle. Known particles may be co-dispersed with unknown particles in a medium and the holographic video microscopy is used to determine properties, such as porosity, of the unknown particles.

A computer-implemented method for analyzing digital holographic microscopy (DHM) data for hematology applications includes receiving a DHM image acquired using a digital holographic microscopy system. The DHM image comprises depictions of one or more cell objects and background. A reference image is generated based on the DHM image. This reference image may then be used to reconstruct a fringe pattern in the DHM image into an optical depth map.

An imaging flow cytometer device includes a housing holding a multi-color illumination source configured for pulsed or continuous wave operation. A microfluidic channel is disposed in the housing and is fluidically coupled to a source of fluid containing objects that flow through the microfluidic channel. A color image sensor is disposed adjacent to the microfluidic channel and receives light from the illumination source that passes through the microfluidic channel. The image sensor captures image frames containing raw hologram images of the moving objects passing through the microfluidic channel. The image frames are subject to image processing to reconstruct phase and/or intensity images of the moving objects for each color. The reconstructed phase and/or intensity images are then input to a trained deep neural network that outputs a phase recovered image of the moving objects. The trained deep neural network may also be trained to classify object types.

A quantum simulator includes a pseudo speckle pattern generator, a main vacuum chamber, an atomic gas supply unit, a light beam generator, a photodetector, and an atom number detector. The pseudo speckle pattern generator generates a pseudo speckle pattern in the inside of the main vacuum chamber by light allowed to enter the inside of the main vacuum chamber through the second window. The pseudo speckle pattern generator includes a controller, a light source, a beam expander, a spatial light modulator, and a lens. The controller sets a modulation distribution of the spatial light modulator based on a two-dimensional pseudo random number pattern.

A method for observing a sample, the sample lying in a sample plane defining radial positions, parameters of the sample being defined at each radial position, the method comprising: a) illuminating the sample using a light source, emitting an incident light wave that propagates toward the sample; b) acquiring, using an image sensor, an image of the sample, said image being formed in a detection plane, the sample being placed between the light source and the image sensor; c) processing the image acquired by the image sensor, so as to obtain an image of the sample, the image of the sample corresponding to a distribution of at least one parameter of the sample describing the sample in the sample plane; wherein the processing of the acquired image comprises implementing an iterative method, followed by applying a supervised machine learning algorithm, so as to obtain an initialization image intended to initialize the iterative method.