The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jun. 30, 2026

Filed:

Sep. 23, 2021
Applicant:

Intel Corporation, Santa Clara, CA (US);

Inventors:

Anbang Yao, Beijing, CN;

Ming Lu, Beijing, CN;

Yikai Wang, Beijing, CN;

Yurong Chen, Beijing, CN;

Attila Tamas Afra, Satu Mare, RO;

Sungye Kim, Folsom, CA (US);

Karthik Vaidyanathan, San Francisco, CA (US);

Assignee:

INTEL CORPORATION, Santa Clara, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 5/70 (2024.01); G06N 3/08 (2023.01); G06N 20/10 (2019.01);
U.S. Cl.
CPC ...
G06T 5/70 (2024.01); G06N 3/08 (2013.01); G06N 20/10 (2019.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
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.


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