公开(公告)号：US11869171B2

公开(公告)日：2024-01-09

申请号：US17090170

申请日：2020-11-05

Applicant: Intel Corporation

Inventor： Anbang Yao ,  Ming Lu ,  Yikai Wang ,  Xiaoming Chen ,  Junjie Huang ,  Tao Lv ,  Yuanke Luo ,  Yi Yang ,  Feng Chen ,  Zhiming Wang ,  Zhiqiao Zheng ,  Shandong Wang

IPC: G06T5/00 ,  G06N3/04

CPC classification number: G06T5/002 ,  G06N3/04 ,  G06T2207/20081 ,  G06T2207/20084

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.

公开(公告)号：US20240127408A1

公开(公告)日：2024-04-18

申请号：US18514252

申请日：2023-11-20

Applicant: Intel Corporation

Inventor： Anbang Yao ,  Ming Lu ,  Yikai Wang ,  Xiaoming Chen ,  Junjie Huang ,  Tao Lv ,  Yuanke Luo ,  Yi Yang ,  Feng Chen ,  Zhiming Wang ,  Zhiqiao Zheng ,  Shandong Wang

IPC: G06T5/00 ,  G06N3/04

CPC classification number: G06T5/002 ,  G06N3/04 ,  G06T2207/20081 ,  G06T2207/20084

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.

公开(公告)号：US20210142448A1

公开(公告)日：2021-05-13

申请号：US17090170

申请日：2020-11-05

Applicant: Intel Corporation

Inventor： Anbang Yao ,  Ming Lu ,  Yikai Wang ,  Xiaoming Chen ,  Junjie Huang ,  Tao Lv ,  Yuanke Luo ,  Yi Yang ,  Feng Chen ,  Zhiming Wang ,  Zhiqiao Zheng ,  Shandong Wang

IPC: G06T5/00 ,  G06N3/04

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.

公开(公告)号：US20210142179A1

公开(公告)日：2021-05-13

申请号：US17090071

申请日：2020-11-05

Applicant: Intel Corporation

Inventor： Xiaoming Chen ,  Anbang Yao ,  Junjie Huang ,  Tao Lv ,  Yuanke Luo

IPC: G06N3/08 ,  G06N3/04 ,  G06N3/063 ,  G06N20/10

Abstract: Embodiments are generally directed to dynamically dividing activations and kernels for improving memory efficiency. An embodiment of a method in a compute engine performing machine learning comprises: receiving, by a convolutional layer of a convolutional neural network (CNN) implemented on the compute engine, a plurality of activation groups contained in an input data, wherein the convolutional layer includes one or more kernel groups and the one or more kernel groups each include a plurality of kernels; determining a plurality of memory efficiency metrics based on the number of activation groups of the plurality of activation groups and the number of kernels of the plurality of kernels; selecting a first optimal number of activation groups and a second optimal number of kernels that are associated with an optimal memory efficiency metric in the plurality of memory efficiency metrics; and performing a convolutional operation on the input data based on the first optimal number and the second optimal number.

公开(公告)号：US10306420B2

公开(公告)日：2019-05-28

申请号：US15564708

申请日：2015-06-03

Applicant: Intel Corporation

Inventor： Shen Zhou ,  Zhijie Sheng ,  Thanunathan Rangarajan ,  Junjie Huang

IPC: G06F21/57 ,  H04W4/04 ,  H04W4/50 ,  H04W4/80 ,  G06F9/455 ,  G06F21/44 ,  H04W4/33

Abstract: Embodiments of self-locating computing devices, systems, and methods are described. In some embodiments, a computing device may include a Wireless Credential Exchange Module (WCEM) to detect one or more location tags and a management engine, coupled to the WCEM, to retrieve information of the one or more location tags from the WCEM, and to provide an asset management server with an identifier of the computing device and the information of the one or more location tags or location information of the computing device. Other embodiments may be described and/or claimed.