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公开(公告)号：US20200082535A1
公开(公告)日：2020-03-12
申请号：US16566155
申请日：2019-09-10
Applicant: Apple Inc.
Inventor： Alexander Lindskog , Michael W. Tao , Alexandre Naaman
Abstract: This disclosure relates to techniques for the robust usage of semantic segmentation information in image processing techniques, e.g., shallow depth of field (SDOF) renderings. Semantic segmentation may be defined as a process of creating a mask over an image, wherein pixels are segmented into a predefined set of semantic classes. Segmentations may be binary (e.g., a ‘person pixel’ or a ‘non-person pixel’) or multi-class (e.g., a pixel may be labelled as: ‘person,’ ‘dog,’ ‘cat,’ etc.). As semantic segmentation techniques grow in accuracy and adoption, it is becoming increasingly important to develop methods of utilizing such segmentations and developing flexible techniques for integrating segmentation information into existing computer vision applications, such as synthetic SDOF renderings, to yield improved results in a wide range of image capture scenarios. In some embodiments, a refinement operation may be employed on a camera device's initial depth, disparity and/or blur estimates that leverages semantic segmentation information.
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公开(公告)号：US20240107162A1
公开(公告)日：2024-03-28
申请号：US18347604
申请日：2023-07-06
Applicant: Apple Inc.
Inventor： Dan C. Lelescu , Rohit Rajiv Ranade , Noah Bedard , Brian McCall , Kathrin Berkner Cieslicki , Michael W. Tao , Robert K. Molholm , Toke Jansen , Vladimir Krneta
IPC: H04N23/67 , G06T7/30 , H04N23/958
CPC classification number: H04N23/67 , G06T7/30 , H04N23/958 , G06T2207/20084
Abstract: A method for image enhancement includes capturing multiple input images of a scene, including at least a first input image having a first field of view (FOV) captured with a first focal depth and a second input image having a second FOV captured with a second focal depth. The input images in the sequence are preprocessed so as to align the images. The aligned images are processed in a neural network, which generates an output image having an extended depth of field encompassing at least the first and second focal depths.
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公开(公告)号：US12262117B2
公开(公告)日：2025-03-25
申请号：US17933941
申请日：2022-09-21
Applicant: Apple Inc.
Inventor： Patrick A. Carroll , Ajay Ramesh , Ashwini Dwarakanath , David A. Silverstein , David R. Pope , Michael W. Tao , Terence N. Tam , Vitanshu Sharma
Abstract: Devices, methods, and non-transitory program storage devices are disclosed herein to perform predictive image sensor cropping operations to improve the performance of video image stabilization operations, especially for high resolution image sensors. According to some embodiments, the techniques include, for each of one or more respective images of a first plurality of images: obtaining image information corresponding to one or more images in the first plurality of images captured prior to the respective image; predicting, for the respective image, an image sensor cropping region to be read out from the first image sensor; and then reading out, into a memory, a first cropped version of the respective image comprising only the predicted image sensor cropping region for the respective image. Then, a first video may be produced based, at least in part, on the first cropped versions of the one or more respective images of the first plurality of images.
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公开(公告)号：US20200082541A1
公开(公告)日：2020-03-12
申请号：US16566082
申请日：2019-09-10
Applicant: Apple Inc.
Inventor： Mark N. Jouppi , Alexander Lindskog , Michael W. Tao
IPC: G06T7/194 , G06T5/00 , G06T5/20 , H04N13/128
Abstract: This disclosure relates to techniques for generating robust depth estimations for captured images using semantic segmentation. Semantic segmentation may be defined as a process of creating a mask over an image, wherein pixels are segmented into a predefined set of semantic classes. Such segmentations may be binary (e.g., a ‘person pixel’ or a ‘non-person pixel’) or multi-class (e.g., a pixel may be labelled as: ‘person,’ ‘dog,’ ‘cat,’ etc.). As semantic segmentation techniques grow in accuracy and adoption, it is becoming increasingly important to develop methods of utilizing such segmentations and developing flexible techniques for integrating segmentation information into existing computer vision applications, such as depth and/or disparity estimation, to yield improved results in a wide range of image capture scenarios. In some embodiments, an optimization framework may be employed to optimize a camera device's initial scene depth/disparity estimates that employs both semantic segmentation and color regularization in a robust fashion.
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公开(公告)号：US20240098368A1
公开(公告)日：2024-03-21
申请号：US17933941
申请日：2022-09-21
Applicant: Apple Inc.
Inventor： Patrick A. Carroll , Ajay Ramesh , Ashwini Dwarakanath , David A. Silverstein , David R. Pope , Michael W. Tao , Terence N. Tam , Vitanshu Sharma
CPC classification number: H04N5/23277 , G06T7/38
Abstract: Devices, methods, and non-transitory program storage devices are disclosed herein to perform predictive image sensor cropping operations to improve the performance of video image stabilization operations, especially for high resolution image sensors. According to some embodiments, the techniques include, for each of one or more respective images of a first plurality of images: obtaining image information corresponding to one or more images in the first plurality of images captured prior to the respective image; predicting, for the respective image, an image sensor cropping region to be read out from the first image sensor; and then reading out, into a memory, a first cropped version of the respective image comprising only the predicted image sensor cropping region for the respective image. Then, a first video may be produced based, at least in part, on the first cropped versions of the one or more respective images of the first plurality of images.
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Patent Agency Ranking
公开(公告)号：US10410327B2
公开(公告)日：2019-09-10
申请号：US15990154
申请日：2018-05-25
Applicant: Apple Inc.
Inventor： Richard D. Seely , Michael W. Tao , Alexander Lindskog , Geoffrey T. Anneheim
Abstract: This disclosure relates to techniques for synthesizing out of focus effects in digital images. Digital single-lens reflex (DSLR) cameras and other cameras having wide aperture lenses typically capture images with a shallow depth of field (SDOF). SDOF photography is often used in portrait photography, since it emphasizes the subject, while deemphasizing the background via blurring. Simulating this kind of blurring using a large depth of field (LDOF) camera may require a large amount of computational resources, i.e., in order to simulate the physical effects of using a wide aperture lens while constructing a synthetic SDOF image. However, cameras having smaller lens apertures, such as mobile phones, may not have the processing power to simulate the spreading of all background light sources in a reasonable amount of time. Thus, described herein are techniques to synthesize out-of-focus background blurring effects in a computationally-efficient manner for images captured by LDOF cameras.
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公开(公告)号：US20180350043A1
公开(公告)日：2018-12-06
申请号：US15990154
申请日：2018-05-25
Applicant: Apple Inc.
Inventor： Richard D. Seely , Michael W. Tao , Alexander Lindskog , Geoffrey T. Anneheim
CPC classification number: G06T5/002 , G06T5/50 , G06T7/90 , G06T2207/10024 , H04N5/23216 , H04N5/23229
Abstract: This disclosure relates to techniques for synthesizing out of focus effects in digital images. Digital single-lens reflex (DSLR) cameras and other cameras having wide aperture lenses typically capture images with a shallow depth of field (SDOF). SDOF photography is often used in portrait photography, since it emphasizes the subject, while deemphasizing the background via blurring. Simulating this kind of blurring using a large depth of field (LDOF) camera may require a large amount of computational resources, i.e., in order to simulate the physical effects of using a wide aperture lens while constructing a synthetic SDOF image. However, cameras having smaller lens apertures, such as mobile phones, may not have the processing power to simulate the spreading of all background light sources in a reasonable amount of time. Thus, described herein are techniques to synthesize out-of-focus background blurring effects in a computationally-efficient manner for images captured by LDOF cameras.
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公开(公告)号：US11526995B2
公开(公告)日：2022-12-13
申请号：US16566082
申请日：2019-09-10
Applicant: Apple Inc.
Inventor： Mark N. Jouppi , Alexander Lindskog , Michael W. Tao
IPC: G06T7/194 , G06T5/00 , G06T5/20 , H04N13/128 , H04N13/156 , H04N13/271 , H04N13/00 , G06T7/50
Abstract: This disclosure relates to techniques for generating robust depth estimations for captured images using semantic segmentation. Semantic segmentation may be defined as a process of creating a mask over an image, wherein pixels are segmented into a predefined set of semantic classes. Such segmentations may be binary (e.g., a ‘person pixel’ or a ‘non-person pixel’) or multi-class (e.g., a pixel may be labelled as: ‘person,’ ‘dog,’ ‘cat,’ etc.). As semantic segmentation techniques grow in accuracy and adoption, it is becoming increasingly important to develop methods of utilizing such segmentations and developing flexible techniques for integrating segmentation information into existing computer vision applications, such as depth and/or disparity estimation, to yield improved results in a wide range of image capture scenarios. In some embodiments, an optimization framework may be employed to optimize a camera device's initial scene depth/disparity estimates that employs both semantic segmentation and color regularization in a robust fashion.
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公开(公告)号：US11250571B2
公开(公告)日：2022-02-15
申请号：US16566155
申请日：2019-09-10
Applicant: Apple Inc.
Inventor： Alexander Lindskog , Michael W. Tao , Alexandre Naaman
Abstract: This disclosure relates to techniques for the robust usage of semantic segmentation information in image processing techniques, e.g., shallow depth of field (SDOF) renderings. Semantic segmentation may be defined as a process of creating a mask over an image, wherein pixels are segmented into a predefined set of semantic classes. Segmentations may be binary (e.g., a ‘person pixel’ or a ‘non-person pixel’) or multi-class (e.g., a pixel may be labelled as: ‘person,’ ‘dog,’ ‘cat,’ etc.). As semantic segmentation techniques grow in accuracy and adoption, it is becoming increasingly important to develop methods of utilizing such segmentations and developing flexible techniques for integrating segmentation information into existing computer vision applications, such as synthetic SDOF renderings, to yield improved results in a wide range of image capture scenarios. In some embodiments, a refinement operation may be employed on a camera device's initial depth, disparity and/or blur estimates that leverages semantic segmentation information.
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公开(公告)号：US10909706B1
公开(公告)日：2021-02-02
申请号：US15996363
申请日：2018-06-01
Applicant: Apple Inc.
Inventor： Geoffrey T. Anneheim , Bruno J. Conejo , Stephane S. Ben Soussan , Michael W. Tao
Abstract: Determining disparity includes obtaining a first image of a scene and a second image of a scene, determining correspondences between one or more pixels of the first image and one or more pixels of the second image, performing local denoising on the correspondences based on at least on a strength and direction of gradient values for the one or more pixels of the first image and the one or more pixels of the second image, and generating a disparity map based on the determined correspondences and local denoising.
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