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    UNSUPERVISED CALIBRATION OF TEMPORAL NOISE REDUCTION FOR VIDEO
    2.
    发明公开
    UNSUPERVISED CALIBRATION OF TEMPORAL NOISE REDUCTION FOR VIDEO 审中-公开

    公开(公告)号：US20240046427A1

    公开(公告)日：2024-02-08

    申请号：US18486554

    申请日：2023-10-13

    Applicant: Intel Corporation

    Inventor： Noam Elron

    IPC: G06T5/00

    CPC classification number: G06T5/002 ,  G06T2207/20081

    Abstract: An unsupervised technique for training a deep learning based temporal noise reducer on unlabeled real-world data. The unsupervised technique can also be used to calibrate the free parameters of a TNR based on algorithmic principles. The training is based on actual real-world video (which may include noise), and not based on video containing artificial or added noise. Using the unsupervised technique to train a TNR allows the TNR to be tailored to the noise statistics of the use-case, resulting in the provision of high quality video with minimal resources.
    The TNR can be based on an uncalibrated TNR's output in time-reverse, as well as the uncalibrated TNR's output in time-forward. The frames used for both the time-forward output and the time-reversed output can be frames from the past. The TNR is calibrated to minimize the difference between its time-forward output and its time-reversed output.

    METHODS AND APPARATUS TO PROCESS IMAGES USING SEGMENTATION
    6.
    发明申请
    METHODS AND APPARATUS TO PROCESS IMAGES USING SEGMENTATION 有权

    公开(公告)号：US20250005765A1

    公开(公告)日：2025-01-02

    申请号：US18342549

    申请日：2023-06-27

    Applicant: Intel Corporation

    Inventor： Dmitry Rudoy ,  Rakefet Kol ,  Noam Elron ,  Noam Levy

    IPC: G06T7/194 ,  G06T3/40 ,  G06T7/11

    Abstract: Systems, apparatus, articles of manufacture, and methods are disclosed to process images using segmentation. An example apparatus includes interface circuitry, machine readable instructions, and programmable circuitry to at least one of instantiate or execute the machine readable instructions to generate a scaled frame from an input video frame, segment, with a neural network, the scaled frame to generate a scaled segmentation map based on the scaled frame, the scaled segmentation map to associate pixels of the scaled frame with ones of a plurality of segments in the scaled frame, and generate an output video frame based on the input video frame and an upscaled version of the scaled segmentation map.

    METHOD AND SYSTEM OF REAL-TIME SUPER-RESOLUTION IMAGE PROCESSING
    7.
    发明申请
    METHOD AND SYSTEM OF REAL-TIME SUPER-RESOLUTION IMAGE PROCESSING 有权

    公开(公告)号：US20250005709A1

    公开(公告)日：2025-01-02

    申请号：US18882214

    申请日：2024-09-11

    Applicant: Intel Corporation

    Inventor： Noam Elron ,  Alexander Itskovich ,  Shahar S Yuval ,  Noam Levy

    IPC: G06T3/4053 ,  G06N3/08 ,  G06T3/4046 ,  G06T5/50 ,  G06T5/70

    Abstract: Example methods, apparatus, systems, and articles directed to real-time super-resolution image processing using neural networks are disclosed. Example apparatus disclosed herein cause a neural network to process an input frame of input video, the input video having a first resolution, the neural network trained to upscale the input frame to a second resolution, the neural network trained to reduce a presence of one or more types of image imperfections in the input frame. Disclosed example apparatus also obtain, from the neural network, an output frame at the second resolution. Disclosed example apparatus further cause the output frame to be presented as part of an output video at the second resolution.

    MINIMAL IMAGE SIGNAL PROCESSING PIPELINE FOR AN EARLY SCENE UNDERSTANDING
    8.
    发明公开
    MINIMAL IMAGE SIGNAL PROCESSING PIPELINE FOR AN EARLY SCENE UNDERSTANDING 审中-公开

    公开(公告)号：US20240249392A1

    公开(公告)日：2024-07-25

    申请号：US18583642

    申请日：2024-02-21

    Applicant: Intel Corporation

    Inventor： Dmitry Rudoy ,  Rakefet Kol ,  Noam Elron ,  Noam Levy

    IPC: G06T5/50 ,  G06T3/40 ,  G06T5/20 ,  G06T7/10

    CPC classification number: G06T5/50 ,  G06T3/40 ,  G06T5/20 ,  G06T7/10 ,  G06T2207/20084 ,  G06T2207/20221

    Abstract: A high-level understanding of the scene captured by a camera allows for the use of scene-level understanding in the processing of the captured image. A downscaled image of a captured scene is generated and used as a basis for artificial intelligence analysis before the full image of the captured scene is processed. The downscaled image is generated concurrently with the capturing of the raw image at the image sensor and before full image signal processor (ISP) processing. Neural networks and other AI algorithms can be applied directly to the downscaled image to perform high-level understanding using minimal resources. The processing of the full scale captured image can be adapted to specific scenarios based on the understanding rather than undergoing all-purpose processing. The high-level understanding is provided to the full image processing pipe for enhancements in image quality, video conferencing, face detection, and other user experiences.

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