Patents by Inventor Qiurui He
Qiurui He has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20250069194Abstract: Systems and methods for identifying a personalized prior within a generative model's latent vector space based on a set of images of a given subject. In some examples, the present technology may further include using the personalized prior to confine the inputs of a generative model to a latent vector space associated with the given subject, such that when the model is tasked with editing an image of the subject (e.g., to perform inpainting to fill in masked areas, improve resolution, or deblur the image), the subject's identifying features will be reflected in the images the model produces.Type: ApplicationFiled: November 13, 2024Publication date: February 27, 2025Inventors: Kfir Aberman, Yotam Nitzan, Orly Liba, Yael Pritch Knaan, Qiurui He, Inbar Mosseri, Yossi Gandelsman, Michal Yarom
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Publication number: 20240430577Abstract: The present disclosure relates to a low-light autofocus technique. One example embodiment includes a method. The method includes receiving an indication of a low-light condition for a camera system. The method also includes determining an extended exposure time for a low-light autofocus procedure of the camera system. Further, the method includes capturing, by the camera system, an extended frame for the low-light autofocus procedure. The extended frame is captured by the camera system using the determined extended exposure time. In addition, the method includes determining, based on the captured extended frame, an in-focus lens setting for a lens of the camera system.Type: ApplicationFiled: September 3, 2024Publication date: December 26, 2024Inventors: Ying Chen Lou, Leung Chun Chan, Kiran Murthy, Qiurui He, Szepo Robert Hung, Sushil Nath
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Patent number: 12169911Abstract: Systems and methods for identifying a personalized prior within a generative model's latent vector space based on a set of images of a given subject. In some examples, the present technology may further include using the personalized prior to confine the inputs of a generative model to a latent vector space associated with the given subject, such that when the model is tasked with editing an image of the subject (e.g., to perform inpainting to fill in masked areas, improve resolution, or deblur the image), the subject's identifying features will be reflected in the images the model produces.Type: GrantFiled: June 14, 2023Date of Patent: December 17, 2024Assignee: GOOGLE LLCInventors: Kfir Aberman, Yotam Nitzan, Orly Liba, Yael Pritch Knaan, Qiurui He, Inbar Mosseri, Yossi Gandelsman, Michal Yarom
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Patent number: 12114078Abstract: The present disclosure relates to a low-light autofocus technique. One example embodiment includes a method. The method includes receiving an indication of a low-light condition for a camera system. The method also includes determining an extended exposure time for a low-light autofocus procedure of the camera system. Further, the method includes capturing, by the camera system, an extended frame for the low-light autofocus procedure. The extended frame is captured by die camera system using the determined extended exposure time. In addition, the method includes determining, based on the captured extended frame, an in-focus lens setting for a lens of the camera system.Type: GrantFiled: October 11, 2019Date of Patent: October 8, 2024Assignee: Google LLCInventors: Ying Chen Lou, Leung Chun Chan, Kiran Murthy, Qiurui He, Szepo Robert Hung, Sushil Nath
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Publication number: 20240320808Abstract: 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.Type: ApplicationFiled: June 5, 2024Publication date: September 26, 2024Inventors: Yicheng Wu, Qiurui He, Tianfan Xue, Rahul Garg, Jiawen Chen, Jonathan T. Barron
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Patent number: 12033309Abstract: 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.Type: GrantFiled: November 9, 2020Date of Patent: July 9, 2024Assignee: Google LLCInventors: Yicheng Wu, Qiurui He, Tianfan Xue, Rahul Garg, Jiawen Chen, Jonathan T. Barron
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Publication number: 20230325998Abstract: Systems and methods for identifying a personalized prior within a generative model's latent vector space based on a set of images of a given subject. In some examples, the present technology may further include using the personalized prior to confine the inputs of a generative model to a latent vector space associated with the given subject, such that when the model is tasked with editing an image of the subject (e.g., to perform inpainting to fill in masked areas, improve resolution, or deblur the image), the subject's identifying features will be reflected in the images the model produces.Type: ApplicationFiled: June 14, 2023Publication date: October 12, 2023Inventors: Kfir Aberman, Yotam Nitzan, Orly Liba, Yael Pritch Knaan, Qiurui He, Inbar Mosseri, Yossi Gandelsman, Michal Yarom
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Patent number: 11721007Abstract: Systems and methods for identifying a personalized prior within a generative model's latent vector space based on a set of images of a given subject. In some examples, the present technology may further include using the personalized prior to confine the inputs of a generative model to a latent vector space associated with the given subject, such that when the model is tasked with editing an image of the subject (e.g., to perform inpainting to fill in masked areas, improve resolution, or deblur the image), the subject's identifying features will be reflected in the images the model produces.Type: GrantFiled: November 8, 2022Date of Patent: August 8, 2023Assignee: Google LLCInventors: Kfir Aberman, Yotam Nitzan, Orly Liba, Yael Pritch Knaan, Qiurui He, Inbar Mosseri, Yossi Gandelsman, Michal Yarom
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Publication number: 20230222636Abstract: Systems and methods for identifying a personalized prior within a generative model's latent vector space based on a set of images of a given subject. In some examples, the present technology may further include using the personalized prior to confine the inputs of a generative model to a latent vector space associated with the given subject, such that when the model is tasked with editing an image of the subject (e.g., to perform inpainting to fill in masked areas, improve resolution, or deblur the image), the subject's identifying features will be reflected in the images the model produces.Type: ApplicationFiled: November 8, 2022Publication date: July 13, 2023Inventors: Kfir Aberman, Yotam Nitzan, Orly Liba, Yael Pritch Knaan, Qiurui He, Inbar Mosseri, Yossi Gandelsman, Michal Yarom
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Publication number: 20220375045Abstract: 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.Type: ApplicationFiled: November 9, 2020Publication date: November 24, 2022Inventors: Yicheng Wu, Qiurui He, Tianfan Xue, Rahul Garg, Jiawen Chen, Jonathan T. Barron
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Publication number: 20220294964Abstract: The present disclosure relates to a low-light autofocus technique. One example embodiment includes a method. The method includes receiving an indication of a low-light condition for a camera system. The method also includes determining an extended exposure time for a low-light autofocus procedure of the camera system. Further, the method includes capturing, by the camera system, an extended frame for the low-light autofocus procedure. The extended frame is captured by die camera system using the determined extended exposure time. In addition, the method includes determining, based on the captured extended frame, an in-focus lens setting for a lens of the camera system.Type: ApplicationFiled: October 11, 2019Publication date: September 15, 2022Inventors: Ying Chen Lou, Leung Chun Chan, Kiran Murthy, Qiurui He, Szepo Robert Hung, Sushil Nath