公开(公告)号：US20220207678A1

公开(公告)日：2022-06-30

申请号：US17482998

申请日：2021-09-23

Applicant: Intel Corporation

Inventor： Anbang Yao ,  Ming Lu ,  Yikai Wang ,  Shandong Wang ,  Yurong Chen ,  Sungye Kim ,  Attila Tamas Afra

IPC: G06T5/50 ,  G06K9/00 ,  G06T7/13 ,  G06N3/02

Abstract: The present disclosure provides an apparatus and method of guided neural network model for image processing. An apparatus may comprise a guidance map generator, a synthesis network and an accelerator. The guidance map generator may receive a first image as a content image and a second image as a style image, and generate a first plurality of guidance maps and a second plurality of guidance maps, respectively from the first image and the second image. The synthesis network may synthesize the first plurality of guidance maps and the second plurality of guidance maps to determine guidance information. The accelerator may generate an output image by applying the style of the second image to the first image based on the guidance information.

公开(公告)号：US20250061172A1

公开(公告)日：2025-02-20

申请号：US18883195

申请日：2024-09-12

Applicant: Intel Corporation

Inventor： Anbang Yao ,  Ming Lu ,  Yikai Wang ,  Scott Janus ,  Sungye Kim

IPC: G06F18/2136 ,  G06T11/00

Abstract: Embodiments are generally directed to methods and apparatuses of spatially sparse convolution module for visual rendering and synthesis. An embodiment of a method for image processing, comprising: receiving an input image by a convolution layer of a neural network to generate a plurality of feature maps; performing spatially sparse convolution on the plurality of feature maps to generate spatially sparse feature maps; and upsampling the spatially sparse feature maps to generate an output image.

公开(公告)号：US20220207293A1

公开(公告)日：2022-06-30

申请号：US17482875

申请日：2021-09-23

Applicant: Intel Corporation

Inventor： Anbang Yao ,  Ming Lu ,  Yikai Wang ,  Scott Janus ,  Sungye Kim

IPC: G06K9/62 ,  G06T11/00

Abstract: Embodiments are generally directed to methods and apparatuses of spatially sparse convolution module for visual rendering and synthesis. An embodiment of a method for image processing, comprising: receiving an input image by a convolution layer of a neural network to generate a plurality of feature maps; performing spatially sparse convolution on the plurality of feature maps to generate spatially sparse feature maps; and upsampling the spatially sparse feature maps to generate an output image.

公开(公告)号：US12124533B2

公开(公告)日：2024-10-22

申请号：US17482875

申请日：2021-09-23

Applicant: Intel Corporation

Inventor： Anbang Yao ,  Ming Lu ,  Yikai Wang ,  Scott Janus ,  Sungye Kim

IPC: G06F18/2136 ,  G06T11/00

CPC classification number: G06F18/2136 ,  G06T11/00 ,  G06T2207/20076 ,  G06T2207/20081

Abstract: Embodiments are generally directed to methods and apparatuses of spatially sparse convolution module for visual rendering and synthesis. An embodiment of a method for image processing, comprising: receiving an input image by a convolution layer of a neural network to generate a plurality of feature maps; performing spatially sparse convolution on the plurality of feature maps to generate spatially sparse feature maps; and upsampling the spatially sparse feature maps to generate an output image.

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：US20240257316A1

公开(公告)日：2024-08-01

申请号：US18615050

申请日：2024-03-25

Applicant: Intel Corporation

Inventor： Anbang Yao ,  Ming Lu ,  Yikai Wang ,  Shandong Wang ,  Yurong Chen ,  Sungye Kim ,  Attila Tamas Afra

IPC: G06T5/50 ,  G06N3/02 ,  G06T7/13 ,  G06V40/16

CPC classification number: G06T5/50 ,  G06N3/02 ,  G06T7/13 ,  G06V40/161 ,  G06V40/171 ,  G06T2207/20084 ,  G06T2207/30201

Abstract: The present disclosure provides an apparatus and method of guided neural network model for image processing. An apparatus may comprise a guidance map generator, a synthesis network and an accelerator. The guidance map generator may receive a first image as a content image and a second image as a style image, and generate a first plurality of guidance maps and a second plurality of guidance maps, respectively from the first image and the second image. The synthesis network may synthesize the first plurality of guidance maps and the second plurality of guidance maps to determine guidance information. The accelerator may generate an output image by applying the style of the second image to the first image based on the guidance information.

公开(公告)号：US11972545B2

公开(公告)日：2024-04-30

申请号：US17482998

申请日：2021-09-23

Applicant: Intel Corporation

Inventor： Anbang Yao ,  Ming Lu ,  Yikai Wang ,  Shandong Wang ,  Yurong Chen ,  Sungye Kim ,  Attila Tamas Afra

IPC: G06T5/50 ,  G06N3/02 ,  G06T7/13 ,  G06V40/16

CPC classification number: G06T5/50 ,  G06N3/02 ,  G06T7/13 ,  G06V40/161 ,  G06V40/171 ,  G06T2207/20084 ,  G06T2207/30201

Abstract: The present disclosure provides an apparatus and method of guided neural network model for image processing. An apparatus may comprise a guidance map generator, a synthesis network and an accelerator. The guidance map generator may receive a first image as a content image and a second image as a style image, and generate a first plurality of guidance maps and a second plurality of guidance maps, respectively from the first image and the second image. The synthesis network may synthesize the first plurality of guidance maps and the second plurality of guidance maps to determine guidance information. The accelerator may generate an output image by applying the style of the second image to the first image based on the guidance information.

公开(公告)号：US20220207656A1

公开(公告)日：2022-06-30

申请号：US17483074

申请日：2021-09-23

Applicant: Intel Corporation

Inventor： Anbang Yao ,  Ming Lu ,  Yikai Wang ,  Yurong Chen ,  Attila Tamas Afra ,  Sungye Kim ,  Karthik Vaidyanathan

IPC: G06T5/00 ,  G06N20/10 ,  G06N3/08

Abstract: Embodiments are generally directed to a Conditional Kernel Prediction Network (CKPN) for image and video de-noising and other related image and video processing applications. Disclosed is an embodiment of a method for de-noising an image or video frame 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 weights for the pixel, the plurality of kernel weights respectively corresponding to pixels within a region surrounding the pixel; adjusting the plurality of kernel weights of the convolutional kernel for the pixel based on convolutional kernels generated respectively for the corresponding pixels within the region surrounding the pixel; and filtering the pixel with the adjusted plurality of kernel weights and pixel values of the corresponding pixels within the region surrounding the pixel to obtain a de-noised pixel.