Image Enhancement via Iterative Refinement based on Machine Learning Models

    公开(公告)号:US20250061551A1

    公开(公告)日:2025-02-20

    申请号:US18939994

    申请日:2024-11-07

    Applicant: Google LLC

    Abstract: A method includes receiving, by a computing device, training data comprising a plurality of pairs of images, wherein each pair comprises an image and at least one corresponding target version of the image. The method also includes training a neural network based on the training data to predict an enhanced version of an input image, wherein the training of the neural network comprises applying a forward Gaussian diffusion process that adds Gaussian noise to the at least one corresponding target version of each of the plurality of pairs of images to enable iterative denoising of the input image, wherein the iterative denoising is based on a reverse Markov chain associated with the forward Gaussian diffusion process. The method additionally includes outputting the trained neural network.

    VIDEO GENERATION METHOD AND DEVICE

    公开(公告)号:US20250054271A1

    公开(公告)日:2025-02-13

    申请号:US18723150

    申请日:2022-12-22

    Applicant: Lemon Inc.

    Abstract: The present disclosure provides a video generation method and device. The video generation method includes: extracting a first image feature from a first image; obtaining a plurality of intermediate image features by means of nonlinear interpolation according to the first image feature and a second image feature, wherein the second image feature is an image feature of a second image; and performing image reconstruction by means of an image generation model based on the first image feature, the second image feature, and the plurality of intermediate image features, so as to generate a target video, wherein the target video is used for presenting a process of a gradual change from the first image to the second image.

    Image processing apparatus, image processing method, and display apparatus based on the same

    公开(公告)号:US12223619B2

    公开(公告)日:2025-02-11

    申请号:US17888366

    申请日:2022-08-15

    Inventor: Seong Gyun Kim

    Abstract: The present disclosure provides an image processing apparatus, and image processing method, and a display apparatus. The image processing apparatus includes a first image processor up-sampling an original low-resolution image on the basis of deep learning-based learning data to generate a first high-resolution image, a second image processor interpolating the original low-resolution image to generate a second high-resolution image, a third image processor generating a difference image between the first high-resolution image and the second high-resolution image, extracting a high frequency component from the difference image, and amplifying the extracted high frequency component, and a fourth image processor adding the amplified high frequency component to the first high-resolution image to generate a target high-resolution image.

    Automated right ventricle medical imaging and computation of clinical parameters

    公开(公告)号:US12217434B2

    公开(公告)日:2025-02-04

    申请号:US18208331

    申请日:2023-06-12

    Inventor: Marina Yaacobi

    Abstract: There is provided a method of processing 2D ultrasound images for computing clinical parameter(s) of a right ventricle (RV), comprising: selecting one 2D ultrasound image of 2D ultrasound images depicting the RV, interpolating an inner contour of an endocardial border of the RV for the selected 2D image, tracking the interpolated inner contour obtained for the one 2D ultrasound image over the 2D images over cardiac cycle(s), computing a RV area of the RV for each respective 2D image according to the tracked interpolated inner contour, identifying a first 2D image depicting an end-diastole (ED) state according to a maximal value of the RV area for the 2D images, and a second 2D US image depicting an end-systole (ES) state according to minimal value of the RV area for the 2D images, and computing clinical parameter(s) of the RV according to the identified first and second 2D images.

    Hierarchical grid interpolation systems and methods

    公开(公告)号:US12106444B2

    公开(公告)日:2024-10-01

    申请号:US17356216

    申请日:2021-06-23

    Applicant: Apple Inc.

    CPC classification number: G06T3/18 G06T3/04 G06T3/4007

    Abstract: An electronic device may include an electronic display to display an image based on processed image data. The electronic device may also include image processing circuitry to determine a hierarchical grid having multiple grid points divided into grid partitions. A first set of grid points associated with a first set of grid partitions may include a first set of mappings to corresponding coordinates of input image data in a source frame. The image processing circuitry may also interpolate between the first set of grid points to determine a second set of grid points of having a second set of mappings to corresponding coordinates of the input image data based on the first set of mappings. The image processing circuitry may also generate the processed image data by applying the first set of mappings and the second set of mappings to the input image data.

    Frame Interpolation Using Both Optical Motion And In-Game Motion

    公开(公告)号:US20240311959A1

    公开(公告)日:2024-09-19

    申请号:US18605789

    申请日:2024-03-14

    Applicant: MediaTek Inc.

    CPC classification number: G06T3/4007 G06T7/248

    Abstract: A frame interpolation method generates an interpolated frame that is temporally between a first frame and a second frame. A first and a second interpolated frames are generated using motion vectors from a first motion estimator and a second motion estimator, respectively. A weighting map is generated based on indications from the first motion estimator. First pixel locations and second pixel locations in the weighting map are assigned weight values of 1 and 0, respectively. A weighted combination is calculated using the weighting map to produce the interpolated frame output, which includes the first pixel locations from the first interpolated frame and the second pixel locations from the second interpolated frame. The first and the second motion estimators may be an optical flow estimator and the game engine renderer, respectively. Alternatively, the first and the second motion estimators may be the game engine renderer and the optical flow estimator, respectively.

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