Abstract: In an apparatus, it is determined whether a covariance matrix calculated based on a plurality of patches is abnormal. In a case where it is determined that the covariance matrix is not abnormal, the covariance matrix is used to perform first correction on pixels included in the plurality of patches. In a case where it is determined that the covariance matrix is abnormal, second correction, which is different from the first correction, is performed on the pixels included in the plurality of patches.

Abstract: A computer-implemented method for image processing is provided. The method includes obtaining data acquired by a medical imaging system. The method also includes normalizing the data. The method further includes de-noising the normalized data utilizing a deep learning-based denoising network. The method even further includes de-normalizing the de-noised data. The method yet further includes generating blended data based on both the data and the de-normalized de-noised data.

Abstract: Images may be captured by a moving image capture device. A reference image and a background image may be selected from the images. The reference image may include depiction of an object, with the object blocking view of the background. The background image may include depiction of the background blocked by the object in the reference image. An object layer may be generated by segmenting the depiction of the object from the reference image. A background layer may be generated by combining the depiction of the background in the background image with the reference image. The background layer may be blurred and combined with the object layer to generate a panning image.

Abstract: Disclosed are systems and associated methods for generating a regularized image from non-uniformly distributed image data based on a curve derived from the image data. The curve is used to reduce the non-uniformity in the image data without losing detail or changing the overall image. Regularizing the image includes obtaining a tree-based representation of the non-uniformly distributed image data, generating a regularization curve that models a particular distribution of the image data, and applying the regularization curve to the tree-based representation in order to select the nodes of the tree-based representation for the decimated image data to render in place of the original image data and the original image data to preserve and render as part of the regularized image. Specifically, the system render the original image data associated with leaf nodes and the decimated image data associated with parent nodes that intersect or are within the regularization curve.

Abstract: Methods, systems, and devices for generating a training set and using the training set to train an artificial intelligence algorithm to detect and blur faces in images of an interior space are described. An example method for generating a training set includes selecting a first image depicting an interior of a room, selecting a second image depicting at least a face of a person, generating, based on a transparency parameter and a roughness parameter, a semi-transparent surface image, selecting an illumination parameter to configure a first illumination level for the first image and a second illumination level for the second image, and generating a first composite image of the training data set by combining the illumination parameter, the first image, the second image, and the semi-transparent surface image.

Abstract: In general, intelligent fuel dispensers are provided. In at least some implementations, an intelligent fuel dispenser can determine customer identities and/or other characteristics and provide customized fueling sessions based on the determined customer identities and/or other characteristics. In at least some implementations, the fuel dispenser includes a touchless interface allowing customers to complete fueling sessions with minimal physical contact with the fuel dispenser.

Abstract: In general, intelligent fuel dispensers are provided. In at least some implementations, an intelligent fuel dispenser can determine customer identities and/or other characteristics and provide customized fueling sessions based on the determined customer identities and/or other characteristics. In at least some implementations, the fuel dispenser includes a touchless interface allowing customers to complete fueling sessions with minimal physical contact with the fuel dispenser.

Abstract: A method of determining volumetric data of a predetermined anatomical feature is described. The method comprising determining volumetric data of one or more anatomical features present in a field of view of a depth sensing camera apparatus, identifying a predetermined anatomical feature as being present in the field of view of the depth sensing camera apparatus, associating the volumetric data of one of the one or more anatomical features with the identified predetermined anatomical feature, and outputting the volumetric data of the predetermined anatomical feature. An apparatus is also described.

Abstract: An information processing method of an embodiment is a processing method of information acquired by imaging performed by a medical image diagnostic apparatus, the information processing method includes the steps of: on the basis of first subject data acquired by the imaging performed by the medical image diagnostic apparatus, acquiring noise data in the first subject data; on the basis of second subject data acquired by the imaging performed by a medical image diagnostic modality same kind as the medical image diagnostic apparatus and the noise data, acquiring synthesized subject data in which noises based on the noise data are added to the second subject data; and acquiring a noise reduction processing model by machine learning using the synthesized subject data and third subject data acquired by the imaging performed by the medical image diagnostic modality.

Abstract: A medical information processing apparatus according to an embodiment includes processing circuitry. The processing circuitry calculates noise intensity from first data acquired during a first scan. The processing circuitry calculates a denoising intensity used for a denoising process based on the noise intensity and the difference between an imaging condition for the first scan and an imaging condition for a second scan, the denoising process being applied to second data obtained by the second scan.

Abstract: Exemplary embodiments are directed to a system for selective enhancement of skin features in an image. The system includes an interface configured to receive as input an original image, and a processing device in communication with the interface. The processing device is configured to process the original image using a neural network to detect one or more skin imperfections in the original image, generate a neural network mask of the original image for the one or more skin imperfections in the original image, generate one or more source patches based on the original image, and, replace the one or more skin imperfections in the original image with the one or more source patches to generate a patched skin image.

Abstract: Exemplary embodiments are directed to a system for selective enhancement of an object in an image. The system includes an interface configured to receive as input an original image, and a processing device in communication with the interface. The processing device is configured to process the original image using a neural network to detect one or more objects in the original image, generate a neural network mask of the original image for the one or more objects in the original image, apply one or more enhancements to the objects associated with the neural network mask, the one or more modules generating an enhanced image including the one or more enhancements to the objects, and generate a combined image, the combined image including the original image combined with the one or more enhancements to the objects of the enhanced image.

Abstract: Methods, systems, and apparatus for image processing are provided. In one aspect, a method includes: acquiring an image to be processed that involves a target object, extracting image noise information and contour information of the target object from the image to be processed, generating a noise distribution image based on the image noise information and the contour information of the target object, and obtaining a target image by performing noise reduction on the image to be processed with the noise distribution image.

Abstract: An image processing apparatus includes a first filter unit and a first composition unit. The first filter unit generates a plurality of results of filter processing for brightness by individually applying each of a plurality of filters for brightness with different frequency bands to a first brightness component image indicating a distribution of brightness components in a first image. The plurality of filters for brightness are spatial filters based on a visual characteristic of a human related to contrast detection. The first composition unit generates brightness contrast influence information indicating a distribution of degrees of influence of brightness contrast in the first image by compositing together the plurality of results of filter processing for brightness.

Abstract: The present disclosure relates to a method of motion segmentation (100) in a video stream. The method comprises the steps of: acquiring (101) a sequence of image frames; dividing (102) a first frame (401) into a plurality of image blocks (403); comparing (103) each image block (403) against a corresponding reference image block (404) and providing a measure of dissimilarity; for image blocks having a measure of dissimilarity less than a threshold: discarding (104a) the image blocks, and for image blocks having a measure of dissimilarity greater than the threshold: keeping (104b) the image blocks and further dividing the image blocks into a new plurality of image blocks (405); repeating the steps of dividing (102) and comparing (103) until a stop condition is met (105a); generating (106) a motion mask (407) indicating areas of movement (408).

Abstract: A fluorescent in-situ hybridization imaging and analysis system includes a flow cell to contain a sample to be exposed to fluorescent probes in a reagent, a fluorescence microscope to obtain sequentially collect a plurality of images of the sample at a plurality of different combinations of imaging parameters, and a data processing system. The data processing system includes an online pre-processing system configured to sequentially receive the images from the fluorescence microscope as the images are collected and perform on-the-fly image pre-processing to remove experimental artifacts of the image and to provide RNA image spot sharpening, and an offline processing system configured to, after the plurality of images are collected, perform registration of images having a same field of view and to decode intensity values in the plurality of images to identify expressed genes.

Abstract: A medical image processing apparatus includes: a first captured image acquisition unit configured to acquire a first captured image obtained by capturing light from an observation target irradiated with light in a first wavelength band, the observation target emitting fluorescence when irradiated with excitation light in a second wavelength band different from the first wavelength band; a second captured image acquisition unit configured to acquire a second captured image obtained by capturing the fluorescence from the observation target irradiated with the excitation light; a first image processor configured to execute image processing on the first captured image; and a second image processor configured to execute image processing including blurring processing of blurring an image on the second captured image.

Abstract: An electronic device includes at least one imaging sensor and at least one processor coupled to the at least one imaging sensor. The at least one imaging sensor is configured to capture a burst of image frames. The at least one processor is configured to generate a low-resolution image from the burst of image frames. The at least one processor is also configured to estimate a blur kernel based on the burst of image frames. The at least one processor is further configured to perform deconvolution on the low-resolution image using the blur kernel to generate a deconvolved image. In addition, the at least one processor is configured to generate a high-resolution image using super resolution (SR) on the deconvolved image.

Abstract: An image processing apparatus includes a memory and a processor coupled to the memory and configured to perform acquiring object area information representing an image area of a target object in an image from an image database, using the object area information to calculate a distance map representing distance values from a contour of the target object inside the image area, and generating, based on the distance map, a line of points representing a curved line reflecting a global shape of the target object.

Abstract: A method of depth-of-field simulation, including receiving a plurality of images, predicting a layer of interest mask of the plurality of images, determining a plurality of mean brightness anchor values of a respective plurality of layers of interest window arrays, setting a plurality of layers of interest set of binary codes, determining a hamming distance between plurality of layers of interest set of binary codes, determining a cost volume based on the hamming distance, resampling a vertical cost based on a vertical ordinal direction the cost volume, resampling a horizontal cost based on a horizontal ordinal direction the cost volume, determining an all-in-focus layer based on the vertical cost and the horizontal cost, determining an out-of-focus layer based on the vertical cost and the horizontal cost and determining a depth of the all-in-focus layer and out-of-focus layer.