Techniques for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system, the techniques including: obtaining input MR data obtained by imaging the subject using the MRI system; generating a plurality of transformed input MR data instances by applying a respective first plurality of transformations to the input MR data; generating a plurality of MR images from the plurality of transformed input MR data instances and the input MR data using a non-linear MR image reconstruction technique; generating an ensembled MR image from the plurality of MR images at least in part by: applying a second plurality of transformations to the plurality of MR images to obtain a plurality of transformed MR images; and combining the plurality of transformed MR images to obtain the ensembled MR image; and outputting the ensembled MR image.

Techniques for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system, the techniques including: obtaining input MR data obtained by imaging the subject using the MRI system; generating a plurality of transformed input MR data instances by applying a respective first plurality of transformations to the input MR data; generating a plurality of MR images from the plurality of transformed input MR data instances and the input MR data using a non-linear MR image reconstruction technique; generating an ensembled MR image from the plurality of MR images at least in part by: applying a second plurality of transformations to the plurality of MR images to obtain a plurality of transformed MR images; and combining the plurality of transformed MR images to obtain the ensembled MR image; and outputting the ensembled MR image.

Opto-electronic devices can evaluate convolutional neural networks with reduced power consumption and higher speeds using optical metamaterial structures. Methods and systems for convolution of an input vector f with a kernel k can include a first optical element that is adjustable according to the input vector f and a second optical element that is adjustable according to the kernel k, where either or both elements can include adjustable optical metasurfaces. In some approaches, the second optical element is adjustable according to a Fourier transform of the kernel k and is interposed between first and second lenses or volumetric metamaterials implementing Fourier and inverse Fourier transforms, respectively.

Systems, computer program products, and methods are described herein for frequency filtering in image analysis for identity verification. The present invention is configured to receive, from a user input device, a request from a user to initiate identity verification for access privileges; receive a first image of a face of the user from the user input device in response to the request, wherein the first image is in a geometric domain; transform, using an image transformation algorithm, the first image from the geometric domain into a frequency domain; process, using a machine learning (ML) subsystem, the first image of the user; authenticate the user based on at least processing the first image; and automatically trigger one or more access privilege protocols in response to authenticating the user.

An electronic device is provided. The electronic device includes a transparent member comprising a transparent material, a display panel disposed under the transparent member and including a plurality of pixels, a biometric sensor disposed under the display panel, and a filter disposed between the display panel and the biometric sensor and covering the biometric sensor.

Techniques for generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system, the techniques include: obtaining input MR spatial frequency data obtained by imaging the subject using the MRI system; generating an MR image of the subject from the input MR spatial frequency data using a neural network model comprising: a pre-reconstruction neural network configured to process the input MR spatial frequency data; a reconstruction neural network configured to generate at least one initial image of the subject from output of the pre-reconstruction neural network; and a post-reconstruction neural network configured to generate the MR image of the subject from the at least one initial image of the subject.

An image processing apparatus includes an acquisition unit that acquires a hologram obtained by imaging a plurality of granules contained within an imaging visual field, a generation unit that generates, from the hologram, phase difference images at positions different from each other in an optical axis direction in a case in which the hologram is captured, a specifying unit that specifies a plurality of image ranges in a direction of a plane intersecting the optical axis direction, which correspond to the plurality of granules, in an averaged image obtained by averaging at least some of the phase difference images, and an extraction unit that extracts the phase difference image at a center position of a corresponding granule in the optical axis direction for each of the plurality of image ranges.

A living body determination device includes: a light irradiation device that irradiates a measuring object with a first light including a plurality of spectrums; a spectroscopic device that disperses a light at intensity depending on a wavelength and outputs the light; an image acquisition device that receives the light output by the spectroscopic device and outputs image information representing brightness depending on the intensity of the light; and a control unit. The control unit, for each spectrum of the first light, acquires image information with respect to the measuring object from the image acquisition device, based on the image information, selects one or more areas, for each of the areas, acquires spectroscopic information, and based on whether the spectroscopic information satisfies a predetermined condition, determines whether the measuring object is a living body.

A method for evaluating an infrared signature present on an object surface, which signature preferably forms a two-dimensional code. Furthermore, a monochrome or multicolour pattern, which reflects light in the visible wavelength range, can be present on the object surface. The infrared signature only absorbs light in the infrared range and can consequently be detected by means of an IR camera. In the method, an infrared light source is switched on and the infrared signature is illuminated with infrared light and an original image is recorded with an infrared camera in this state. The original image or a further-processed image based thereon is then filtered by a high-pass filter, the contrast in the image being increased indirectly or directly after the high-pass filtering. The infrared signature can finally be evaluated in the image processed in this way.

Generating magnetic resonance (MR) images of a subject from MR data obtained by a magnetic resonance imaging (MRI) system by: generating first and second sets of one or more MR images from first and second input MR data; aligning the first and second sets of MR images using a neural network model comprising first and second neural networks, the aligning comprising: estimating, using the first neural network, a first transformation between the first and second sets of MR images; generating a first updated set of MR images from the second set of MR images using the first transformation; estimating, using the second neural network, a second transformation between the first set and the first updated set of MR images; and aligning the first set of MR images and the second set of MR images at least in part by using the first transformation and the second transformation.