A method for lens-free imaging of a sample or objects within the sample uses multi-height iterative phase retrieval and rotational field transformations to perform wide FOV imaging of pathology samples with clinically comparable image quality to a benchtop lens-based microscope. The solution of the transport-of-intensity (TIE) equation is used as an initial guess in the phase recovery process to speed the image recovery process. The holographically reconstructed image can be digitally focused at any depth within the object FOV (after image capture) without the need for any focus adjustment, and is also digitally corrected for artifacts arising from uncontrolled tilting and height variations between the sample and sensor planes. In an alternative embodiment, a synthetic aperture approach is used with multi-angle iterative phase retrieval to perform wide FOV imaging of pathology samples and increase the effective numerical aperture of the image.

Techniques related to generating holographic images are discussed. Such techniques include application of a hybrid system including a pre-trained deep neural network and a subsequent iterative process using a sui table propagation model to generate diffraction pattern image data for a target holographic image such that the diffraction pattern image data is to generate a holographic image when implemented via a holographic display.

A method of computing a hologram by determining the wavefronts at the approximate observer eye position that would be generated by a real version of an object to be reconstructed. In normal computer generated holograms, one determines the wavefronts needed to reconstruct an object; this is not done directly in the present invention. Instead, one determines the wavefronts at an observer window that would be generated by a real object located at the same position of the reconstructed object. One can then back-transforms these wavefronts to the hologram to determine how the hologram needs to be encoded to generate these wavefronts. A suitably encoded hologram can then generate a reconstruction of the three-dimensional scene that can be observed by placing one's eyes at the plane of the observer window and looking through the observer window.

A computer calculates interference fringe phase estimated value data (30) of a phase-restored object image by performing iterative approximation calculation using interference fringe intensity data (10) measured by a digital holography apparatus and interference fringe phase initial value data (20), which is an estimated initial phase value of the image of the object. The interference fringe phase initial value data (20) is calculated by an initial phase estimator (300). The initial phase estimator (300) is constructed by implementing machine learning using interference fringe intensity data and the like for learning. The computer acquires reconfigured intensity data (40) and reconfigured phase data (50) by performing optical wave propagation calculation using the interference fringe phase estimation value data (30) of the image of the object acquired through phase restoration, and the interference fringe intensity data (10) used as input data for the initial phase estimator (300). This provides an iterative approximation calculation method and the like capable of making an initial value of a solution used in the iterative approximation calculation method a value close to the true value.

Disclosed are a holographic display method and a holographic display device. The holographic display method includes: acquiring an area of Nth diffraction order corresponding to an eye position; according to the area of Nth diffraction order, calculating a holographic complex amplitude distribution corresponding to a window of Nth diffraction order to obtain window hologram information, a function of the holographic complex amplitude distribution being expressed by C(m,n)=A(m,n)*exp[−iφ(m,n)/N]; encoding the window hologram information; and according to the encoded window hologram information, loading the encoded window hologram information in the area of Nth diffraction order to display a hologram.

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.

Techniques related to generating holographic images are discussed. Such techniques include application of a pre-trained deep neural network to a target holographic image to generate a feedback strength value for error feedback in an iterative propagation feedback model and generating a diffraction pattern image corresponding to the target holographic image by applying the iterative propagation feedback model based on the target holographic image and using the feedback strength value.

Provided is a method of generating a hologram, the method including generating a kernel and a neural network configured to model an aberration of a holographic display device, obtaining second image data output from the neural network to which first image data obtained by propagating a first hologram based on the kernel is input, updating the kernel and the neural network based on comparing the second image data and predetermined image data, and generating a second hologram based on the kernel and the neural network.

There is provided a holographic projector comprising a hologram engine and a controller. The hologram engine is arranged to provide a hologram comprising a plurality of hologram pixels. Each hologram pixel has a respective hologram pixel value. The controller is arranged to selectively-drive a plurality of light-modulating pixels so as to display the hologram. Displaying the hologram comprises displaying each hologram pixel value on a contiguous group of light-modulating pixels of the plurality of light-modulating pixels such that there is a one-to-many pixel correlation between the hologram and the plurality of light-modulating pixels.

A method and system for improving the control of a holographic projection system in order to meet, or to attempt to meet, one or more targets or aims for a holographically reconstructed image that is produced by the holographic projection system. The target, or aim, may concern the luminance of part or all of the holographically reconstructed image.