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:
Jan. 09, 2024

Filed:

Nov. 05, 2020
Applicant:

Intel Corporation, Santa Clara, CA (US);

Inventors:

Anbang Yao, Beijing, CN;

Ming Lu, Beijing, CN;

Yikai Wang, Beijing, CN;

Xiaoming Chen, Shanghai, CN;

Junjie Huang, Guangdong, CN;

Tao Lv, Shanghai, CN;

Yuanke Luo, Shanghai, CN;

Yi Yang, Shanghai, CN;

Feng Chen, Shanghai, CN;

Zhiming Wang, Shanghai, CN;

Zhiqiao Zheng, Guangdong Sheng, CN;

Shandong Wang, Beijing, CN;

Assignee:

INTEL CORPORATION, Santa Clara, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 5/00 (2006.01); G06N 3/04 (2023.01);
U.S. Cl.
CPC ...
G06T 5/002 (2013.01); G06N 3/04 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
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.


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