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:
Mar. 01, 2022

Filed:

May. 16, 2019
Applicant:

Here Global B.v., Eindhoven, NL;

Inventors:

Abinaya Manimaran, Chicago, IL (US);

Krishna Kumar Balakrishnan, Chicago, IL (US);

David Johnston Lawlor, Chicago, IL (US);

Anish Mittal, San Francisco, CA (US);

Zhanwei Chen, Richmond, CA (US);

Assignee:

HERE Global B.V., Eindhoven, NL;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 3/40 (2006.01); G06T 7/73 (2017.01); G06N 3/08 (2006.01); G06T 5/00 (2006.01);
U.S. Cl.
CPC ...
G06T 3/4053 (2013.01); G06N 3/08 (2013.01); G06T 5/003 (2013.01); G06T 5/009 (2013.01); G06T 7/73 (2017.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01);
Abstract

An approach is provided for generating a super-resolution image as a higher resolution version of an input image. The approach, for example, involves determining a set of tasks to be performed on the input image to facilitate generating the super-resolution image. The approach also involves selecting a combination of loss functions, wherein each loss function of the combination of loss functions is respectively a task-specific neural network pre-trained to perform a corresponding one of the set of tasks. The approach also involves training the super resolution neural network using the combination of loss functions as one or more layers of the super resolution neural network. The approach also involves using the trained super resolution neural network to generate the super-resolution image as a higher resolution version of the input image.


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