Hue map generation for highlight recovery

    公开(公告)号:US10855964B2

    公开(公告)日:2020-12-01

    申请号:US16116838

    申请日:2018-08-29

    Applicant: Apple Inc.

    Abstract: Embodiments relate to generation of hue maps for highlight recovery of an input image. An image having a plurality of color channels is obtained at a first resolution lower than a resolution of the input image. A hue for each color channel for each pixel is determined, using a pixel value for that color channel and pixel values for the plurality of color channels in the first image. Weights are determined for each pixel for each color channel, based on hues for the pixel and pixel values for the pixel in the first image. A plurality of candidate hue maps are generated, based on the weights and pixel values in the first image in a patch surrounding the pixel for the plurality of color channels.

    Image pipeline with dual demosaicing circuit for efficient image processing

    公开(公告)号:US10692177B2

    公开(公告)日:2020-06-23

    申请号:US16100709

    申请日:2018-08-10

    Applicant: Apple Inc.

    Abstract: Embodiments relate to a first demosaicing circuit and a second demosaicing circuit that can perform demosaicing of image data. The first demosaicing circuit processes received image data to generate a first demosaiced image for obtaining statistic information on the received image data. The second demosaicing circuit performs demosaicing of the received image data to generate a second demosaiced image. A processing circuit pipeline performs at least one of resampling, noise processing, color processing and output rescaling performed on the second demosaiced image based on the statistics information obtained from the first demosaiced image.

    Configurable Convolution Engine
    4.
    发明申请

    公开(公告)号:US20200184000A1

    公开(公告)日:2020-06-11

    申请号:US16791926

    申请日:2020-02-14

    Applicant: Apple Inc.

    Abstract: Embodiments of the present disclosure relate to a configurable convolution engine that receives configuration information to perform convolution or its variant operations on streaming input data of various formats. To process streaming input data, input data of multiple channels are received and stored in an input buffer circuit in an interleaved manner. Data values of the interleaved input data are retrieved and forwarded to multiplier circuits where multiplication with a corresponding filter element of a kernel is performed. Varying number of kernels with different sizes and sparsity can also be used for the convolution operations.

    CONFIGURABLE CONVOLUTION ENGINE FOR INTERLEAVED CHANNEL DATA

    公开(公告)号:US20200167889A1

    公开(公告)日:2020-05-28

    申请号:US16664120

    申请日:2019-10-25

    Applicant: Apple Inc.

    Abstract: Embodiments relate to a configurable convolution engine that receives configuration information to perform convolution and other deep machine learning operations on streaming input data of various formats. The convolution engine may include two convolution circuits that each generate a stream of values by applying convolution kernels to input data. The stream of values may each define one or more channels of image data. A channel merge circuit combines the streams of values from each convolution circuit in accordance with a selected mode of operation. In one mode, the first and second streams from the convolution circuits are merged into an output stream having the combined channels of the first and second streams in an interleaved manner. In another mode, the first stream from the first convolution circuit is fed into the input of the second convolution circuit.

    Configurable convolution engine
    6.
    发明授权

    公开(公告)号:US10606918B2

    公开(公告)日:2020-03-31

    申请号:US16664096

    申请日:2019-10-25

    Applicant: Apple Inc.

    Abstract: Embodiments of the present disclosure relate to a configurable convolution engine that receives configuration information to perform convolution or its variant operations on streaming input data of various formats. To process streaming input data, input data of multiple channels are received and stored in an input buffer circuit in an interleaved manner. Data values of the interleaved input data are retrieved and forwarded to multiplier circuits where multiplication with a corresponding filter element of a kernel is performed. Varying number of kernels with different sizes and sparsity can also be used for the convolution operations.

    AUTOFOCUSING IMAGES USING MOTION DETECTION
    7.
    发明申请

    公开(公告)号:US20200053378A1

    公开(公告)日:2020-02-13

    申请号:US16100809

    申请日:2018-08-10

    Applicant: Apple Inc.

    Abstract: Embodiments of the present disclosure relate to autofocusing of images using motion vectors generated by an image signal processor of a device. An image being processed may include one or more motion detection windows associated with a motion vector as well as one or more autofocus windows. An autofocus window that follows a motion detection window by at least a threshold vertical distance may be selected, for example, to account for a period of time or latency for determining a motion vector of the motion detection window. The device may perform autofocusing by shifting location of the selected autofocus window.

    IMAGE STATISTICS FOR MOTION DETECTION
    8.
    发明申请

    公开(公告)号:US20200053377A1

    公开(公告)日:2020-02-13

    申请号:US16100772

    申请日:2018-08-10

    Applicant: Apple Inc.

    Abstract: Embodiments of the present disclosure relate to generating motion vectors. An image signal processor includes a statistics circuit and a vector correlation analysis circuit. The statistics circuit determines image statistics such as vectors representing sums of pixel values of rows or columns of blocks of an image. Additionally, the statistics circuit may mix or aggregate sums of multiple color components. The vector correlation analysis performs cross-correlation between vectors of a current image and reference vectors of a prior image to determine cross-correlation scores. The vector correlation analysis generates a motion vector by identifying shifts in horizontal and vertical directions corresponding to peak values of cross-correlation scores.

    Convolution engine for merging interleaved channel data

    公开(公告)号:US10325342B2

    公开(公告)日:2019-06-18

    申请号:US15499524

    申请日:2017-04-27

    Applicant: Apple Inc.

    Abstract: Embodiments relate to a configurable convolution engine that receives configuration information to perform convolution and other deep machine learning operations on streaming input data of various formats. The convolution engine may include two convolution circuits that each generate a stream of values by applying convolution kernels to input data. The stream of values may each define multiple channels of image data. A channel merge circuit interleaves the streams of values from the convolution circuits to generate an output stream of output values. The output stream includes the data multiple channels of each input stream arranged in an interleaved manner.

    Multi-illumination white balance circuit with thumbnail image processing

    公开(公告)号:US12231782B2

    公开(公告)日:2025-02-18

    申请号:US18127296

    申请日:2023-03-28

    Applicant: Apple Inc.

    Abstract: An image processing circuit for multi-illumination white balance with thumbnail processing. The image processing circuit determines a set of initial weights for a source pixel in a thumbnail image by determining component values for multiple color channels of the source pixel. The image processing circuit determines a set of weights for the source pixel in a weight map for the thumbnail image. Each weight in the set of weights is determined based on corresponding initial weights from the set of initial weights. Each weight in the set of weights represents an intensity level of a respective chrominance class of multiple chrominance classes for the source pixel. The image processing circuit applies the set of weights to values of the color channels of the source pixel to generate color component values of the color channels of a target pixel in a target thumbnail image.

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