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公开(公告)号：US20220375042A1
公开(公告)日：2022-11-24
申请号：US17626069
申请日：2020-11-13
申请人： Google LLC
发明人： Rahul Garg , Neal Wadhwa , Pratul Preeti Srinivasan , Tianfan Xue , Jiawen Chen , Shumian Xin , Jonathan T. Barron
摘要： A method includes obtaining dual-pixel image data that includes a first sub-image and a second sub-image, and generating an in-focus image, a first kernel corresponding to the first sub-image, and a second kernel corresponding to the second sub-image. A loss value may be determined using a loss function that determines a difference between (i) a convolution of the first sub-image with the second kernel and (ii) a convolution of the second sub-image with the first kernel, and/or a sum of (i) a difference between the first sub-image and a convolution of the in-focus image with the first kernel and (ii) a difference between the second sub-image and a convolution of the in-focus image with the second kernel. Based on the loss value and the loss function, the in-focus image, the first kernel, and/or the second kernel, may be updated and displayed.
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公开(公告)号：US20230037958A1
公开(公告)日：2023-02-09
申请号：US17786065
申请日：2020-12-24
申请人： GOOGLE LLC
发明人： Orly Liba , Rahul Garg , Neal Wadhwa , Jon Barron , Hayato Ikoma
IPC分类号： G06T7/50
摘要： A system includes a computing device. The computing device is configured to perform a set of functions. The set of functions includes receiving an image, wherein the image comprises a two-dimensional array of data. The set of functions includes extracting, by a two-dimensional neural network, a plurality of two-dimensional features from the two-dimensional array of data. The set of functions includes generating a linear combination of the plurality of two-dimensional features to form a single three-dimensional input feature. The set of functions includes extracting, by a three-dimensional neural network, a plurality of three-dimensional features from the single three-dimensional input feature. The set of functions includes determining a two-dimensional depth map. The two-dimensional depth map contains depth information corresponding to the plurality of three-dimensional features.
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公开(公告)号：US20210183089A1
公开(公告)日：2021-06-17
申请号：US16759808
申请日：2017-11-03
申请人： Google LLC
发明人： Neal Wadhwa , Jonathan Barron , Rahul Garg , Pratul Srinivasan
摘要： Example embodiments allow for training of artificial neural networks (ANNs) to generate depth maps based on images. The ANNs are trained based on a plurality of sets of images, where each set of images represents a single scene and the images in such a set of images differ with respect to image aperture and/or focal distance. An untrained ANN generates a depth map based on one or more images in a set of images. This depth map is used to generate, using the image(s) in the set, a predicted image that corresponds, with respect to image aperture and/or focal distance, to one of the images in the set. Differences between the predicted image and the corresponding image are used to update the ANN. ANNs tramed in this manner are especially suited for generating depth maps used to perform simulated image blur on small-aperture images.)
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公开(公告)号：US12008738B2
公开(公告)日：2024-06-11
申请号：US17626069
申请日：2020-11-13
申请人： Google LLC
发明人： Rahul Garg , Neal Wadhwa , Pratul Preeti Srinivasan , Tianfan Xue , Jiawen Chen , Shumian Xin , Jonathan T. Barron
摘要： A method includes obtaining dual-pixel image data that includes a first sub-image and a second sub-image, and generating an in-focus image, a first kernel corresponding to the first sub-image, and a second kernel corresponding to the second sub-image. A loss value may be determined using a loss function that determines a difference between (i) a convolution of the first sub-image with the second kernel and (ii) a convolution of the second sub-image with the first kernel, and/or a sum of (i) a difference between the first sub-image and a convolution of the in-focus image with the first kernel and (ii) a difference between the second sub-image and a convolution of the in-focus image with the second kernel. Based on the loss value and the loss function, the in-focus image, the first kernel, and/or the second kernel, may be updated and displayed.
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公开(公告)号：US11949848B2
公开(公告)日：2024-04-02
申请号：US17422734
申请日：2019-11-19
申请人： Google LLC
发明人： Mira Leung , Steve Perry , Fares Alhassen , Abe Stephens , Neal Wadhwa
IPC分类号： H04N13/271 , G06T7/55
CPC分类号： H04N13/271 , G06T7/55 , G06T2207/10028
摘要： Implementations described herein relate to a computer-implemented method that includes capturing image data using one or more cameras, wherein the image data includes a primary image and associated depth values. The method further includes encoding the image data in an image format. The encoded image data includes the primary image encoded in the image format and image metadata that includes a device element that includes a profile element indicative of an image type and a first camera element, wherein the first camera element includes an image element and a depth map based on the depth values. The method further includes, after the encoding, storing the image data in a file container based on the image format. The method further includes causing the primary image to be displayed.
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公开(公告)号：US11599747B2
公开(公告)日：2023-03-07
申请号：US17090948
申请日：2020-11-06
申请人： Google LLC
发明人： Yael Pritch Knaan , Marc Levoy , Neal Wadhwa , Rahul Garg , Sameer Ansari , Jiawen Chen
IPC分类号： G06K9/62
摘要： Apparatus and methods related to using machine learning to determine depth maps for dual pixel images of objects are provided. A computing device can receive a dual pixel image of at least a foreground object. The dual pixel image can include a plurality of dual pixels. A dual pixel of the plurality of dual pixels can include a left-side pixel and a right-side pixel that both represent light incident on a single dual pixel element used to capture the dual pixel image. The computing device can be used to train a machine learning system to determine a depth map associated with the dual pixel image. The computing device can provide the trained machine learning system.
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公开(公告)号：US20220132095A1
公开(公告)日：2022-04-28
申请号：US17422734
申请日：2019-11-19
申请人： Google LLC
发明人： Mira Leung , Steve Perry , Fares Alhassen , Abe Stephens , Neal Wadhwa
IPC分类号： H04N13/271 , G06T7/55
摘要： Implementations described herein relate to a computer-implemented method that includes capturing image data using one or more cameras, wherein the image data includes a primary image and associated depth values. The method further includes encoding the image data in an image format. The encoded image data includes the primary image encoded in the image format and image metadata that includes a device element that includes a profile element indicative of an image type and a first camera element, wherein the first camera element includes an image element and a depth map based on the depth values. The method further includes, after the encoding, storing the image data in a file container based on the image format. The method further includes causing the primary image to be displayed.
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公开(公告)号：US20200242788A1
公开(公告)日：2020-07-30
申请号：US16652568
申请日：2017-12-05
申请人： Google LLC
发明人： David Jacobs , Rahul Garg , Yael Pritch Knaan , Neal Wadhwa , Marc Levoy
摘要： A camera may capture an image of a scene and use the image to generate a first and a second subpixel image of the scene. The pair of subpixel images may be represented by a first set of subpixels and a second set of subpixels from the image respectively. Each pixel of the image may include two green subpixels that are respectively represented in the first and second subpixel images. The camera may determine a disparity between a portion of the scene as represented by the pair of subpixel images and may estimate a depth map of the scene that indicates a depth of the portion relative to other portions of the scene based on the disparity and a baseline distance between the two green subpixels. A new version of the image may be generated with a focus upon the portion and with the other portions of the scene blurred.
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公开(公告)号：US12118697B2
公开(公告)日：2024-10-15
申请号：US17753279
申请日：2021-02-24
申请人： Google LLC
发明人： Rahul Garg , Neal Wadhwa
IPC分类号： G06T5/73 , G06T5/50 , H04N25/704
CPC分类号： G06T5/73 , G06T5/50 , H04N25/704
摘要： A method includes obtaining split-pixel image data including a first sub-image and a second sub-image. The method also includes determining, for each respective pixel of the split-pixel image data, a corresponding position of a scene feature represented by the respective pixel relative to a depth of field, and identifying, based on the corresponding positions, out-of-focus pixels. The method additionally includes determining, for each respective out-of-focus pixel, a corresponding pixel value based on the corresponding position, a location of the respective out-of-focus pixel within the split-pixel image data, and at least one of: a first value of a corresponding first pixel in the first sub-image or a second value of a corresponding second pixel in the second sub-image. The method further includes generating, based on the corresponding pixel values, an enhanced image having an extended depth of field.
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10.发明公开公开(公告)号：US20230342890A1
公开(公告)日：2023-10-26
申请号：US17726720
申请日：2022-04-22
申请人： Google LLC
CPC分类号： G06T5/005 , G06T7/11 , G06T11/20 , G06T2207/20016 , G06T2207/10016 , G06T2210/12
摘要： Systems and methods for augmenting images can utilize one or more image augmentation models and one or more texture transfer blocks. The image augmentation model can process input images and one or more segmentation masks to generate first output data. The first output data and the one or more segmentation masks can be processed with the texture transfer block to generate an augmented image. The input image can depict a scene with one or more occlusions, and the augmented image can depict the scene with the one or more occlusions replaced with predicted pixel data.
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