Many embodiments can comprise a system. The system can comprise one or more processors and one or more storage devices. The one or more storage devices can be configured to store computing instructions that, when executed, cause the processor to receive a plurality of images of an object, the plurality of images comprising different views of the object from around the object; iteratively align one or more images within one or more subsets of the plurality of images until the object is aligned from image to image within the one or more subsets of the plurality of images; and selectively align respective images of the one or more subsets to each other to produce a surround image. Other embodiments are disclosed herein.

The present disclosure generally relates to the field of deep learning technologies. An apparatus for generating a plurality of correlation images may include a feature extracting unit configured to receive a training image and extracting at least one or more of feature from the training image to generate a first feature image based on the training image; a normalizer configured to normalize the first feature image and generate a second feature image; and a shift correlating unit configured to perform a plurality of translational shifts on the second feature image to generate a plurality of shifted images, correlate each of the plurality of shifted images with the second feature image to generate the plurality of correlation images.

Disclosed herein are an apparatus and method for detecting a keypoint based on deep learning robust to scale changes based on information change across receptive fields. The apparatus for detecting a keypoint based on deep learning robust to scale changes based on information change across receptive fields includes a feature extractor for extracting a feature from an input image based on a pre-trained deep learning neural network, an information accumulation pyramid module for outputting, from the feature, at least two filter responses corresponding to receptive fields having different scales, an information change detection module for calculating an information change between the at least two filter responses, a keypoint detection module for creating a score map having a keypoint probability of each pixel based on the information change, and a continuous scale estimation module for estimating a scale of a receptive field having a biggest information change for each pixel.

An image processing apparatus for performing image quality processing on an image includes: a memory configured to store one or more instructions; and a processor configured to execute the one or more instructions stored in the memory to: obtain a first image by downscaling an input image by using a downscale network; extract first feature information corresponding to the first image by using a feature extraction network; obtain a second image by performing image quality processing on the first image based on the first feature information, by using an image quality processing network; and obtain an output image by upscaling the second image, extracting second feature information corresponding to the input image, and performing image quality processing on the upscaled second image based on the second feature information, by using an upscale network.

A method and system is disclosed for displaying acquired image data of a subject. The image may include a high resolution of clear image based on the acquired image data. The image may assist in evaluating the subject.

This description relates to image feature extraction. In some examples, a system can include a keypoint detector and a feature list generator. The keypoint detector can be configured to upsample a keypoint score map to produce an upsampled keypoint score map. The keypoint score map can include feature scores indicative of a likelihood of at least one feature being present at keypoints in an image. The feature list generator can be configured to identify a subset of keypoints of the keypoints in the image using the feature scores of the up sampled keypoint score map, determine descriptors for the subset of keypoints based on a feature description map, and generate a keypoint descriptor map for the image based on the determined descriptors.

In a method for operating a magnetic resonance (MR) apparatus, MR raw-data is acquired from an acquisition region of a patient for a sampling region of k-space using a MR sequence that employs ultrashort echo times; a first MR image dataset is reconstructed from the MR raw-data of the k-space region; a second MR image dataset is reconstructed from the MR raw-data in a central subregion of the sampling region in k-space; a resolution of the second MR image dataset is interpolated to increase the resolution of the second MR image dataset to a resolution of the first magnetic resonance image dataset; and the first and second MR image datasets are combined to obtain an output MR image dataset.

A multichannel interpolator has an input that receives input data that consists of interleaved channel data from a plurality of data channels. A block random access memory (BRAM) stores data samples from the input data received from the input. Input control logic receives the data samples from the input and places the data samples into the BRAM. Interpolator logic interpolates the data samples to produce output data. The output data is interpolated at an interpolation ratio programmed by a user. The interpolator logic includes a phase generator that calculates a value indicating the interpolation ratio, and a fractional template block that provides a fractional template used to interpolate the data samples to produce the output data, the fraction template block selecting, based on the value calculated by the phase generator. The fractional template is used to interpolate the data samples to produce the output data. Output control logic accesses the BRAM to provide the interpolator logic with the data samples stored in the BRAM as the data samples are needed to interpolate the data samples to produce the output data.

This disclosure relates generally to speckle image analysis, and, more particularly, to a system and method for imaging of localized and heterogenous dynamics using laser speckle. Existing speckle analysis techniques do not offer the capability to achieve both the dynamic phenomenon which carries over a specific time duration and localizing the extent of the activity at a single, chosen instant of time simultaneously. The present disclosure records an image stack consisting of N speckle images sequentially over a period, divides the image stack into a spatial window and a temporal window, converts the speckle intensity data comprised in the spatial window into a column vector. Construct a diagonal matrix and extract a singular value from the diagonal matrix, then defines a speckle intensity correlation metric using the plurality of singular values, defines a speckle activity and generates a speckle contrast image by graphically plotting the speckle activity values.

A computer-implemented method for a classification and training a neural network includes receiving input at the neural network, wherein the input includes a plurality of resolution inputs of varying resolutions, outputting a plurality of feature tensors for each corresponding resolution of the plurality of resolution inputs, fusing the plurality of feature tensors utilizing upsampling or down sampling for the vary resolutions, utilizing an equilibrium solver to identify one or more prediction vectors from the plurality of feature tensors, and outputting a loss in response to the one or more prediction vectors.