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公开(公告)号：US09916637B1
公开(公告)日：2018-03-13
申请号：US15499448
申请日：2017-04-27
Applicant: Apple Inc.
Inventor： Maxim W. Smirnov
CPC classification number: G06T1/20 , G06T3/4007 , H04N5/20 , H04N5/357 , H04N9/646
Abstract: Embodiments of the present disclosure generally relate to image signal processing logic, and in particular, to separating an undecimated image signal data to create two components with lower resolution and full-resolution, generating an interpolation guidance information based on the two components created by separation, forming a difference image data representing the difference between the chroma and luma values of each pixel and its neighboring pixels, and merging the processed image data from the processing pipelines with the unprocessed image data using the interpolation guidance information generated. The generation of the interpolation guidance information is based on determining distances between pixel values from a group comprising pixels from interpolation nodes, pixels diagonally located adjacent to the interpolation nodes, pixels horizontally adjacent to the interpolation nodes, and pixels vertically adjacent to the interpolation nodes.
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Patent Agency Ranking
公开(公告)号：US09811892B1
公开(公告)日：2017-11-07
申请号：US15198414
申请日：2016-06-30
Applicant: Apple Inc.
Inventor： D. Amnon Silverstein , David R. Pope , Simon W. Butler , Maxim W. Smirnov
CPC classification number: G06T5/40 , G06T3/4007 , G06T3/4023 , G06T5/003 , G06T5/20 , G06T5/50 , G06T2207/10024 , G06T2207/20016 , G06T2207/20221
Abstract: Embodiments of the present disclosure generally relate to image signal processing logic, and in particular, to separating an undecimated image signal data to create two components with lower resolution and full-resolution, generating an interpolation guidance information based on the two components created by separation, forming a difference image data representing the difference between the chroma and luma values of each pixel and its neighboring pixels, and merging the processed image data from the processing pipelines with the unprocessed image data using the interpolation guidance information generated. The generation of the interpolation guidance information is based on determining distances between pixel values from a group comprising pixels from interpolation nodes, pixels diagonally located adjacent to the interpolation nodes, pixels horizontally adjacent to the interpolation nodes, and pixels vertically adjacent to the interpolation nodes.
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公开(公告)号：US11010870B2
公开(公告)日：2021-05-18
申请号：US16848287
申请日：2020-04-14
Applicant: Apple Inc.
Inventor： Maxim W. Smirnov , David R. Pope , Oren Kerem , Elena Lamburn
Abstract: Embodiments relate to two stage multi-scale processing of an image. A first stage processing circuitry generates an unscaled single color version of the image that undergoes noise reduction before generating a high frequency component of the unscaled single color version. A scaler generates a first downscaled version of the image comprising a plurality of color components. A second stage processing circuitry generates a plurality of sequentially downscaled images based on the first downscaled version. The second stage processing circuitry processes the first downscaled version and the downscaled images to generate a processed version of the first downscaled version. The unscaled single color high frequency component and the processed version of the first downscaled version of the image are merged to generate a processed version of the image.
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公开(公告)号：US10262401B2
公开(公告)日：2019-04-16
申请号：US15499659
申请日：2017-04-27
Applicant: Apple Inc.
Inventor： Maxim W. Smirnov , D. Amnon Silverstein
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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公开(公告)号：US20200242731A1
公开(公告)日：2020-07-30
申请号：US16848287
申请日：2020-04-14
Applicant: Apple Inc.
Inventor： Maxim W. Smirnov , David R. Pope , Oren Kerem , Elena Lamburn
Abstract: Embodiments relate to two stage multi-scale processing of an image. A first stage processing circuitry generates an unscaled single color version of the image that undergoes noise reduction before generating a high frequency component of the unscaled single color version. A scaler generates a first downscaled version of the image comprising a plurality of color components. A second stage processing circuitry generates a plurality of sequentially downscaled images based on the first downscaled version. The second stage processing circuitry processes the first downscaled version and the downscaled images to generate a processed version of the first downscaled version. The unscaled single color high frequency component and the processed version of the first downscaled version of the image are merged to generate a processed version of the image.
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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
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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