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.

The present invention relates to a system and method for digital holographic microscopy. According to an aspect of the invention there is provided an off-axis digital holographic microscope comprising: a light emitter configured to provide a divergent light beam; a sensor position to receive light from the light emitter in a first path and a second path, and thereby to detect a holographic image; a reflector positioned partially in the divergent light beam so that light that encounters the reflector extends towards the sensor in the first path, and light that does not encounter the reflector extends towards the sensor in the second path; and a support structure configured to support a sample in the first path or the second path.

A spatial modulator is provided on a plane conjugate to a sample plane on which a sample is to be placed. The spatial modulator spatially modulates illumination light irradiated to the sample 2 or object light that has passed through or that has been reflected by the sample. A dark-field optical system removes the non-scattered light component of the first object light affected by the spatial light modulator so as to generate second object light. An image sensor records a hologram based on the second object light. A calculation processing apparatus combines complex amplitude information based on the modulation pattern supplied to the spatial light modulator and complex amplitude information based on the hologram with respect to the second object light so as to acquire a phase distribution originating from the sample.

A method and system for performing incoherent color holographic microscopy imaging using light of various wavelengths, including modulating radiation at each wavelength to form two beams and detecting their intensity at a detector. The two beams include phase information that is retrieved from the phase shifted intensity recorded at the detector and holographic information is determined from the detected modulation of the two beams for each color. A processor is configured to receive the holographic information via a signal generated by the detector and the processor further generates a three-dimensional image of a target.

A method for manufacturing a radial or azimuthal polarization conversion component comprises the steps of: placing a holographic recording material between two right-angle prisms, wherein the holographic recording material is divided into at least four sector-shaped areas and is partially shielded, and only one of the sector-shaped areas is exposed each time; allowing a recording light to pass through the right-angle prisms and the exposed sector-shaped area of the holographic recording material and to interfere with a reflected object light on the holographic recording material; rotating the holographic recording material to expose the other sector-shaped areas one by one to be constructed for manufacturing volume holograms with diffraction angles of 48.19 degrees, 60 degrees or about 85 degrees.

A method of performing phase retrieval and holographic image reconstruction of an imaged sample includes obtaining a single hologram intensity image of the sample using an imaging device. The single hologram intensity image is back-propagated to generate a real input image and an imaginary input image of the sample with image processing software, wherein the real input image and the imaginary input image contain twin-image and/or interference-related artifacts. A trained deep neural network is provided that is executed by the image processing software using one or more processors and configured to receive the real input image and the imaginary input image of the sample and generate an output real image and an output imaginary image in which the twin-image and/or interference-related artifacts are substantially suppressed or eliminated. In some embodiments, the trained deep neural network simultaneously achieves phase-recovery and auto-focusing significantly extending the DOF of holographic image reconstruction.

An optical measurement system comprises a polarization beam splitter for dividing an incident beam into a reference beam and a measurement beam, a first beam splitter for reflecting the measurement beam to form a first reflected measurement beam, a spatial light modulator for modulating the first reflected measurement beam to form a modulated measurement beam, a condenser lens for focusing the modulated measurement beam to an object to form a penetrating measurement beam, an objective lens for converting the penetrating measurement beam into a parallel measurement beam, a mirror for reflecting the parallel measurement beam to form an object beam, a second beam splitter for reflecting the reference beam to a path coincident with that of the object beam, and a camera for receiving an interference signal generated by the reference beam and the object beam to generate an image of the object.

A system for the detection and classification of live microorganisms in a sample includes a light source and an incubator holding one or more sample-containing growth plates. A translation stage moves the image sensor and/or the growth plate(s) along one or more dimensions to capture time-lapse holographic images of microorganisms. Image processing software executed by a computing device captures time-lapse holographic images of the microorganisms or clusters of microorganisms on the one or more growth plates. The image processing software is configured to detect candidate microorganism colonies in reconstructed, time-lapse holographic images based on differential image analysis. The image processing software includes one or more trained deep neural networks that process the time-lapsed image(s) of candidate microorganism colonies to detect true microorganism colonies and/or output a species associated with each true microorganism colony.

A cell area extraction unit (241) extracts a cell area in a phase image that is created based on a hologram obtained by in-line holographic microscope (IHM). A background value acquisition unit (242) obtains a background value from phase values at a plurality of positions outside the cell area. An intracellular phase value acquisition unit (243) averages a plurality of phase values on a sampling line set at a position close to the periphery of a cell, while avoiding a central portion in which the phase value may be lowered in the cell area, to obtain an intracellular phase value. A phase change amount calculation unit (244) obtains the difference between the intracellular phase value and the background value. A phase change amount determination unit (245) compares the value of the difference with thresholds in two levels to determine whether the cell is in an undifferentiated state or an undifferentiation deviant state. It is thereby possible to automatically make a correct determination while removing the influence of a theoretical measurement error by IHM.

An optical method of characterizing an object comprises providing an object to be characterized, the object having at least one nanoscale feature; illuminating the object with coherent plane wave optical radiation having a wavelength larger than the nanoscale feature; capturing a diffraction intensity pattern of the radiation which is scattered by the object; supplying the diffraction intensity pattern to a neural network trained with a training set of diffraction intensity patterns corresponding to other objects with a same nanoscale feature as the object to be characterized, the neural network configured to recover information about the object from the diffraction intensity pattern; and making a characterization of the object based on the recovered information.