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    Green Non-Uniformity Correction
    71.
    发明申请
    Green Non-Uniformity Correction 有权
    绿色不均匀校正

    公开(公告)号：US20130321676A1

    公开(公告)日：2013-12-05

    申请号：US13889937

    申请日：2013-05-08

    Applicant: APPLE INC.

    Inventor： D. Amnon Silverstein ,  Suk Hwan Lim ,  Sheng Lin ,  Guy Côté

    IPC: H04N9/73

    CPC classification number: H04N9/73 ,  H04N5/142 ,  H04N9/045 ,  H04N9/735 ,  H04N2209/046

    Abstract: Systems and methods for correcting green channel non-uniformity (GNU) are provided. In one example, GNU may be corrected using energies between the two green channels (Gb and Gr) during green interpolation processes for red and green pixels. Accordingly, the processes may be efficiently employed through implementation using demosaic logic hardware. In addition, the green values may be corrected based on low-pass-filtered values of the green pixels (Gb and Gr). Additionally, green post-processing may provide some defective pixel correction on interpolated greens by correcting artifacts generated through enhancement algorithms.

    Abstract translation: 提供了校正绿色通道不均匀性（GNU）的系统和方法。 在一个示例中，可以在用于红色和绿色像素的绿色插值处理期间使用两个绿色通道（Gb和Gr）之间的能量来校正GNU。 因此，可以通过使用去马赛克逻辑硬件的实现来有效地采用该过程。 此外，绿色值可以基于绿色像素的低通滤波值（Gb和Gr）来校正。 此外，绿色后处理可以通过校正通过增强算法产生的伪影来对内插果实提供一些缺陷像素校正。

    SYSTEMS AND METHODS FOR REDUCING FIXED PATTERN NOISE IN IMAGE DATA
    72.
    发明申请
    SYSTEMS AND METHODS FOR REDUCING FIXED PATTERN NOISE IN IMAGE DATA 有权

    公开(公告)号：US20250097596A1

    公开(公告)日：2025-03-20

    申请号：US18970459

    申请日：2024-12-05

    Applicant: Apple Inc.

    Inventor： Guy Cote ,  D. Amnon Silverstein ,  Suk Hwan Lim ,  Sheng Lin ,  Haitao Guo

    IPC: H04N25/67

    Abstract: The present disclosure generally relates to systems and methods for image data processing. In certain embodiments, an image processing pipeline may be configured to receive a frame of the image data having a plurality of pixels acquired using a digital image sensor. The image processing pipeline may then be configured to determine a first plurality of correction factors that may correct each pixel in the plurality of pixels for fixed pattern noise. The first plurality of correction factors may be determined based at least in part on fixed pattern noise statistics that correspond to the frame of the image data. After determining the first plurality of correction factors, the image processing pipeline may be configured to configured to apply the first plurality of correction factors to the plurality of pixels, thereby reducing the fixed pattern noise present in the plurality of pixels.

    Auto-Focus Engine Architecture for Image Signal Processor
    74.
    发明申请
    Auto-Focus Engine Architecture for Image Signal Processor 有权

    公开(公告)号：US20220303480A1

    公开(公告)日：2022-09-22

    申请号：US17829363

    申请日：2022-06-01

    Applicant: Apple Inc.

    Inventor： Sheng Lin ,  D. Amnon Silverstein ,  David R. Pope

    IPC: H04N5/347 ,  H04N5/367 ,  H04N5/232 ,  G06T5/00 ,  H04N9/64 ,  H04N9/04 ,  H04N5/345

    Abstract: Embodiments relate to image signal processors (ISP) that include binner circuits that down-sample an input image. An input image may include a plurality of pixels. The output image of the binner circuit may include a reduced number of pixels. The binner circuit may include a plurality of different operation modes. In a bin mode, the binner circuit may blend a subset of input pixel values to generate an output pixel quad. In a skip mode, the binner circuit may select one of the input pixel values as the output pixel. The selection may be performed randomly to avoid aliasing. In a luminance mode, the binner circuit may take a weighted average of a subset of pixel values having different colors. In a color value mode, the binner circuit may select one of the colors in a subset of pixel values as an output pixel value.

    Focusing for Virtual and Augmented Reality Systems
    75.
    发明申请
    Focusing for Virtual and Augmented Reality Systems 有权

    公开(公告)号：US20210235054A1

    公开(公告)日：2021-07-29

    申请号：US17227187

    申请日：2021-04-09

    Applicant: Apple Inc.

    Inventor： D. Amnon Silverstein

    IPC: H04N13/117 ,  G02B27/01 ,  G06F3/01

    Abstract: Methods and apparatus for focusing in virtual reality (VR) or augmented reality (AR) devices based on gaze tracking information are described. Embodiments of a VR/AR head-mounted display (HMD) may include a gaze tracking system for detecting position and movement of the user's eyes. For AR applications, gaze tracking information may be used to direct external cameras to focus in the direction of the user's gaze so that the cameras focus on objects at which the user is looking. For AR or VR applications, the gaze tracking information may be used to adjust the focus of the eye lenses so that the virtual content that the user is currently looking at on the display has the proper vergence to match the convergence of the user's eyes.

    Focusing for virtual and augmented reality systems
    76.
    发明授权
    Focusing for virtual and augmented reality systems 有权

    公开(公告)号：US10979685B1

    公开(公告)日：2021-04-13

    申请号：US15965539

    申请日：2018-04-27

    Applicant: Apple Inc.

    Inventor： D. Amnon Silverstein

    IPC: H04N13/117 ,  G02B27/01 ,  G06F3/01

    Abstract: Methods and apparatus for focusing in virtual reality (VR) or augmented reality (AR) devices based on gaze tracking information are described. Embodiments of a VR/AR head-mounted display (HMD) may include a gaze tracking system for detecting position and movement of the user's eyes. For AR applications, gaze tracking information may be used to direct external cameras to focus in the direction of the user's gaze so that the cameras focus on objects at which the user is looking. For AR or VR applications, the gaze tracking information may be used to adjust the focus of the eye lenses so that the virtual content that the user is currently looking at on the display has the proper vergence to match the convergence of the user's eyes.

    Image data conversion
    77.
    发明授权
    Image data conversion 有权

    公开(公告)号：US10375368B2

    公开(公告)日：2019-08-06

    申请号：US15198376

    申请日：2016-06-30

    Applicant: Apple Inc.

    Inventor： D. Amnon Silverstein

    IPC: H04N9/04 ,  G06T3/40 ,  H04N9/76 ,  G06T1/20 ,  H04N5/33 ,  H04N5/365

    Abstract: Embodiments of the present disclosure relate to an apparatus for converting image data from a Bayer format image to a four-plane image format using two memory channels. An example apparatus includes an interface for receiving the Bayer image including repeating pixel groups, where each pixel group includes a first pixel type, a second pixel type, a third pixel type, and a fourth pixel type. The apparatus also includes a memory and a circuit to write the Bayer image to the memory as four-plane data. The four-plane data includes pixels of the first type and the third type in the Bayer image that are written via the first memory channel, and pixels of the second type and the fourth type in the Bayer image that are written via the second memory channel. Embodiments also relate to converting three sensor image data to a Bayer format image using the two memory channels.

    Noise Reduction Using Sequential Use of Multiple Noise Models
    78.
    发明申请
    Noise Reduction Using Sequential Use of Multiple Noise Models 审中-公开

    公开(公告)号：US20180315172A1

    公开(公告)日：2018-11-01

    申请号：US15499659

    申请日：2017-04-27

    Applicant: Apple Inc.

    Inventor： Maxim W. Smirnov ,  D. Amnon Silverstein

    IPC: G06T5/10 ,  G06T7/00

    CPC classification number: G06T5/10 ,  G06T7/0002 ,  G06T2207/20028 ,  G06T2207/20172

    Abstract: Embodiments of the present disclosure relate to performing noise reduction on an input image by first filtering the input image based on coarse noise models of pixels and then subsequently filtering the filtered input image based on finer noise models. The finer noise models use the same or more number of neighboring pixels than the coarse noise filters. The first filtering and subsequent filtering of a pixel in the input image use Mahalanobis distances between the pixel and its neighboring pixels. By performing iterations of filtering using more refined noise models, the noise reduction in the input image can be performed more efficiently and effectively.

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