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    Dark flash photography with a stereo camera
    1.
    发明授权
    Dark flash photography with a stereo camera 有权

    公开(公告)号：US11039122B2

    公开(公告)日：2021-06-15

    申请号：US16120666

    申请日：2018-09-04

    Applicant: Google LLC

    Inventor： Tianfan Xue ,  Jian Wang ,  Jiawen Chen ,  Jonathan Barron

    IPC: H04N13/25 ,  H04N13/254 ,  H04N5/235

    Abstract: Scenes can be imaged under low-light conditions using flash photography. However, the flash can be irritating to individuals being photographed, especially when those individuals' eyes have adapted to the dark. Additionally, portions of images generated using a flash can appear washed-out or otherwise negatively affected by the flash. These issues can be addressed by using a flash at an invisible wavelength, e.g., an infrared and/or ultraviolet flash. At the same time a scene is being imaged, at the invisible wavelength of the invisible flash, the scene can also be imaged at visible wavelengths. This can include simultaneously using both a standard RGB camera and a modified visible-plus-invisible-wavelengths camera (e.g., an “IR-G-UV” camera). The visible and invisible image data can then be combined to generate an improved visible-light image of the scene, e.g., that approximates a visible light image of the scene, had the scene been illuminated during daytime light conditions.

    Machine Learning Models for Example-Guided Image Inpainting
    2.
    发明申请
    Machine Learning Models for Example-Guided Image Inpainting 有权

    公开(公告)号：US20250037251A1

    公开(公告)日：2025-01-30

    申请号：US18717098

    申请日：2022-01-13

    Applicant: Google LLC

    Inventor： Orly Liba ,  Kfir Aberman ,  Wei Xiong ,  David Futschik ,  Yael Pritch Knaan ,  Daniel Sýkora ,  Tianfan Xue

    IPC: G06T5/77 ,  G06T5/50 ,  G06T5/60 ,  G06V10/74 ,  G06V10/77 ,  G06V10/774 ,  G06V10/82

    Abstract: A method includes obtaining an input image having a region to be inpainted, an indication of the region to be inpainted, and a guide image. The method also includes determining, by an encoder model, a first latent representation of the input image and a second latent representation of the guide image, and generating a combined latent representation based on the first latent representation and the second latent representation. The method additionally includes generating, by a style generative adversarial network model and based on the combined latent representation, an intermediate output image that includes inpainted image content for the region to be inpainted in the input image. The method further includes generating, based on the input image, the indication of the region, and the intermediate output image, an output image representing the input image with the region to be inpainted including the inpainted image content from the intermediate output image.

    Dual exposure control in a camera system
    5.
    发明授权
    Dual exposure control in a camera system 有权

    公开(公告)号：US11800235B2

    公开(公告)日：2023-10-24

    申请号：US17629992

    申请日：2019-08-19

    Applicant: GOOGLE LLC

    Inventor： Ryan Geiss ,  Marc S. Levoy ,  Samuel William Hasinoff ,  Tianfan Xue

    IPC: H04N23/73 ,  G06F3/04847

    CPC classification number: H04N23/73 ,  G06F3/04847

    Abstract: Apparatus and methods related to applying lighting models to images of objects are provided. A neural network can be trained to apply a lighting model to an input image. The training of the neural network can utilize confidence learning that is based on light predictions and prediction confidence values associated with lighting of the input image. A computing device can receive an input image of an object and data about a particular lighting model to be applied to the input image. The computing device can determine an output image of the object by using the trained neural network to apply the particular lighting model to the input image of the object.

    Learning-based lens flare removal
    6.
    发明授权
    Learning-based lens flare removal 有权

    公开(公告)号：US12033309B2

    公开(公告)日：2024-07-09

    申请号：US17625994

    申请日：2020-11-09

    Applicant: Google LLC

    Inventor： Yicheng Wu ,  Qiurui He ,  Tianfan Xue ,  Rahul Garg ,  Jiawen Chen ,  Jonathan T. Barron

    IPC: G06T7/80 ,  G06T3/40 ,  G06T5/10 ,  G06T5/20 ,  G06T5/80

    CPC classification number: G06T5/80 ,  G06T3/40 ,  G06T5/10 ,  G06T5/20 ,  G06T7/80 ,  G06T2207/20081 ,  G06T2207/20084

    Abstract: A method includes obtaining an input image that contains a particular representation of lens flare, and processing the input image by a machine learning model to generate a de-flared image that includes the input image with at least part of the particular representation of lens flare removed. The machine learning (ML) model may be trained by generating training images that combine respective baseline images with corresponding lens flare images. For each respective training image, a modified image may be determined by processing the respective training image by the ML model, and a loss value may be determined based on a loss function comparing the modified image to a corresponding baseline image used to generate the respective training image. Parameters of the ML model may be adjusted based on the loss value determined for each respective training image and the loss function.

    Defocus blur removal and depth estimation using dual-pixel image data
    7.
    发明授权
    Defocus blur removal and depth estimation using dual-pixel image data 有权

    公开(公告)号：US12008738B2

    公开(公告)日：2024-06-11

    申请号：US17626069

    申请日：2020-11-13

    Applicant: Google LLC

    Inventor： Rahul Garg ,  Neal Wadhwa ,  Pratul Preeti Srinivasan ,  Tianfan Xue ,  Jiawen Chen ,  Shumian Xin ,  Jonathan T. Barron

    IPC: G06T5/73 ,  G06T5/50 ,  G06T7/50

    CPC classification number: G06T5/73 ,  G06T5/50 ,  G06T7/50

    Abstract: 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.

    Learning-Based Lens Flare Removal
    8.
    发明公开
    Learning-Based Lens Flare Removal 审中-公开

    公开(公告)号：US20240320808A1

    公开(公告)日：2024-09-26

    申请号：US18734000

    申请日：2024-06-05

    Applicant: Google LLC

    Inventor： Yicheng Wu ,  Qiurui He ,  Tianfan Xue ,  Rahul Garg ,  Jiawen Chen ,  Jonathan T. Barron

    IPC: G06T5/80 ,  G06T3/40 ,  G06T5/10 ,  G06T5/20 ,  G06T7/80

    CPC classification number: G06T5/80 ,  G06T3/40 ,  G06T5/10 ,  G06T5/20 ,  G06T7/80 ,  G06T2207/20081 ,  G06T2207/20084

    Abstract: A method includes obtaining an input image that contains a particular representation of lens flare, and processing the input image by a machine learning model to generate a de-flared image that includes the input image with at least part of the particular representation of lens flare removed. The machine learning (ML) model may be trained by generating training images that combine respective baseline images with corresponding lens flare images. For each respective training image, a modified image may be determined by processing the respective training image by the ML model, and a loss value may be determined based on a loss function comparing the modified image to a corresponding baseline image used to generate the respective training image. Parameters of the ML model may be adjusted based on the loss value determined for each respective training image and the loss function.

    Learning-Based Lens Flare Removal
    10.
    发明申请
    Learning-Based Lens Flare Removal 有权

    公开(公告)号：US20220375045A1

    公开(公告)日：2022-11-24

    申请号：US17625994

    申请日：2020-11-09

    Applicant: Google LLC

    Inventor： Yicheng Wu ,  Qiurui He ,  Tianfan Xue ,  Rahul Garg ,  Jiawen Chen ,  Jonathan T. Barron

    IPC: G06T5/00 ,  G06T5/20 ,  G06T7/80 ,  G06T5/10 ,  G06T3/40

    Abstract: A method includes obtaining an input image that contains a particular representation of lens flare, and processing the input image by a machine learning model to generate a de-flared image that includes the input image with at least part of the particular representation of lens flare removed. The machine learning (ML) model may be trained by generating training images that combine respective baseline images with corresponding lens flare images. For each respective training image, a modified image may be determined by processing the respective training image by the ML model, and a loss value may be determined based on a loss function comparing the modified image to a corresponding baseline image used to generate the respective training image. Parameters of the ML model may be adjusted based on the loss value determined for each respective training image and the loss function.

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