Patents by Inventor Zhiqiao Zheng

Zhiqiao Zheng 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).

  • ADAPTIVE DEFORMABLE KERNEL PREDICTION NETWORK FOR IMAGE DE-NOISING
    Publication number: 20240127408
    Abstract: Embodiments are generally directed to an adaptive deformable kernel prediction network for image de-noising. An embodiment of a method for de-noising an image by a convolutional neural network implemented on a compute engine, the image including a plurality of pixels, the method comprising: for each of the plurality of pixels of the image, generating a convolutional kernel having a plurality of kernel values for the pixel; generating a plurality of offsets for the pixel respectively corresponding to the plurality of kernel values, each of the plurality of offsets to indicate a deviation from a pixel position of the pixel; determining a plurality of deviated pixel positions based on the pixel position of the pixel and the plurality of offsets; and filtering the pixel with the convolutional kernel and pixel values of the plurality of deviated pixel positions to obtain a de-noised pixel.
    Type: Application
    Filed: November 20, 2023
    Publication date: April 18, 2024
    Applicant: Intel Corporation
    Inventors: Anbang Yao, Ming Lu, Yikai Wang, Xiaoming Chen, Junjie Huang, Tao Lv, Yuanke Luo, Yi Yang, Feng Chen, Zhiming Wang, Zhiqiao Zheng, Shandong Wang
  • Adaptive deformable kernel prediction network for image de-noising
    Patent number: 11869171
    Abstract: Embodiments are generally directed to an adaptive deformable kernel prediction network for image de-noising. An embodiment of a method for de-noising an image by a convolutional neural network implemented on a compute engine, the image including a plurality of pixels, the method comprising: for each of the plurality of pixels of the image, generating a convolutional kernel having a plurality of kernel values for the pixel; generating a plurality of offsets for the pixel respectively corresponding to the plurality of kernel values, each of the plurality of offsets to indicate a deviation from a pixel position of the pixel; determining a plurality of deviated pixel positions based on the pixel position of the pixel and the plurality of offsets; and filtering the pixel with the convolutional kernel and pixel values of the plurality of deviated pixel positions to obtain a de-noised pixel.
    Type: Grant
    Filed: November 5, 2020
    Date of Patent: January 9, 2024
    Assignee: INTEL CORPORATION
    Inventors: Anbang Yao, Ming Lu, Yikai Wang, Xiaoming Chen, Junjie Huang, Tao Lv, Yuanke Luo, Yi Yang, Feng Chen, Zhiming Wang, Zhiqiao Zheng, Shandong Wang
  • ADAPTIVE DEFORMABLE KERNEL PREDICTION NETWORK FOR IMAGE DE-NOISING
    Publication number: 20210142448
    Abstract: Embodiments are generally directed to an adaptive deformable kernel prediction network for image de-noising. An embodiment of a method for de-noising an image by a convolutional neural network implemented on a compute engine, the image including a plurality of pixels, the method comprising: for each of the plurality of pixels of the image, generating a convolutional kernel having a plurality of kernel values for the pixel; generating a plurality of offsets for the pixel respectively corresponding to the plurality of kernel values, each of the plurality of offsets to indicate a deviation from a pixel position of the pixel; determining a plurality of deviated pixel positions based on the pixel position of the pixel and the plurality of offsets; and filtering the pixel with the convolutional kernel and pixel values of the plurality of deviated pixel positions to obtain a de-noised pixel.
    Type: Application
    Filed: November 5, 2020
    Publication date: May 13, 2021
    Applicant: Intel Corporation
    Inventors: Anbang Yao, Ming Lu, Yikai Wang, Xiaoming Chen, Junjie Huang, Tao Lv, Yuanke Luo, Yi Yang, Feng Chen, Zhiming Wang, Zhiqiao Zheng, Shandong Wang