An apparatus which analyses a depth of a holographic image is provided. The apparatus includes an acquisition unit that acquires a hologram, a restoration unit that restores a three-dimensional holographic image by irradiating the hologram with a light source, an image sensing unit that senses a depth information image of the restored holographic image, and an analysis display unit that analyzes a depth quality of the holographic image, based on the sensed depth information image, and the image sensing unit uses a lensless type of photosensor.

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 modultor based on a two-dimensional pseudo random number pattern.

Digital holography is one of the most widely used label-free microscopy techniques in biomedical imaging. Recovery of the missing phase information of a hologram is an important step in holographic image reconstruction. A convolutional recurrent neural network (RNN)-based phase recovery approach is employed that uses multiple holograms, captured at different sample-to-sensor distances to rapidly reconstruct the phase and amplitude information of a sample, while also performing autofocusing through the same trained neural network. The success of this deep learning-enabled holography method is demonstrated by imaging microscopic features of human tissue samples and Papanicolaou (Pap) smears. These results constitute the first demonstration of the use of recurrent neural networks for holographic imaging and phase recovery, and compared with existing methods, the presented approach improves the reconstructed image quality, while also increasing the depth-of-field and inference speed.

A method for aquatic microplastic identification is provided. The method includes steps as follows: emitting a laser beam from a laser source, such that the laser beam passes through a liquid sample with MP samples in a sample channel and then a polarizer and is received by polarization camera; capturing a sample image of the MP samples by the polarization camera, wherein the sample image comprises interference patterns resulting from superposition of object and reference waves; and feeding the interference patterns into a morphology analyzing module for real-time tracking and analyzing by using a lightweight convolutional neural network (CNN) model, so as to classify the MP samples.

We describe methods of mass-producing full color, 3D holograms, potentially incorporating a personalized image, which are particularly suitable for security purposes. Broadly speaking in embodiments a method generates, electronically, an interlaced image comprising a set of different views of a 3D object from different angles. This is projected onto a diffusing screen using coherent light and mapped from the screen into an angularly encoded object beam using a lenticular array. The different views in the angularly encoded object beam are then recorded simultaneously into holographic film using a reference beam.

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.

A deep learning framework, termed Fourier Imager Network (FIN) is disclosed that can perform end-to-end phase recovery and image reconstruction from raw holograms of new types of samples, exhibiting success in external generalization. The FIN architecture is based on spatial Fourier transform modules with the deep neural network that process the spatial frequencies of its inputs using learnable filters and a global receptive field. FIN exhibits superior generalization to new types of samples, while also being much faster in its image inference speed, completing the hologram reconstruction task in 0.04 s per 1 mm.sup.2 of the sample area. Beyond holographic microscopy and quantitative phase imaging applications, FIN and the underlying neural network architecture may open up various new opportunities to design broadly generalizable deep learning models in computational imaging and machine vision fields.

An optically trapped atom transfer tweezer may be provided that includes: an optical modulator which modulates incident light and generates a first hologram; a first lens which images the first hologram on an intermediate image plane and generates a first holographic image having any potential shape; a second lens which re-images the first holographic image on an entrance pupil of a third lens; the third lens which re-images a second hologram generated by the re-imaging of the second lens on a plane where an optically trapped atom array exists; a photographing device which captures optically trapped cold atoms from a second holographic image generated on the plane where an optically trapped atom array exists; and a controller which controls the optical modulator to adjust the second holographic image on the basis of the optically trapped atom image captured by the photographing device. As a result of this, the optically trapped atom array can be easily transferred to any position.

An in-vitro method for determining a cell type of a white blood cell in a biological sample does so without labeling, wherein a microscopy apparatus images the cell, and physical parameters of the cell are ascertained from the image of the cell by an automated image analysis. The cell type of the white blood cell is determined on the basis of the physical parameters and on the basis of principal component analysis parameters (PCA parameters) , wherein the principal component analysis parameters comprise linear combinations of at least some of the physical parameters.

An in-vitro method for determining a cell type of a white blood cell in a biological sample does so without labeling, wherein a microscopy apparatus images the cell, and physical parameters of the cell are ascertained from the image of the cell by an automated image analysis. The cell type of the white blood cell is determined on the basis of the physical parameters and on the basis of principal component analysis parameters (PCA parameters), wherein the principal component analysis parameters comprise linear combinations of at least some of the physical parameters.