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. 06, 2023

Filed:

Oct. 16, 2020
Applicant:

Microsoft Technology Licensing, Llc, Redmond, WA (US);

Inventors:

Luming Liang, Redmond, WA (US);

Ilya Dmitriyevich Zharkov, Sammamish, WA (US);

Vivek Pradeep, Redmond, WA (US);

Faezeh Amjadi, Redmond, WA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 5/00 (2006.01); G06N 3/08 (2023.01); G06T 3/40 (2006.01); G06T 5/50 (2006.01); G06N 3/045 (2023.01);
U.S. Cl.
CPC ...
G06T 5/009 (2013.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06T 3/40 (2013.01); G06T 5/50 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20212 (2013.01);
Abstract

A computational photography system is described herein including a guidance system and a detail enhancement system. The guidance system uses a first neural network that maps an original image provided by an image sensor to a guidance image, which represents a color-corrected and lighting-corrected version of the original image. A combination unit combines the original image and the guidance image to produce a combined image. A detail-enhancement system then uses a second neural network to map the combined image to a predicted image. The predicted image supplements the guidance provided by the first neural network by sharpening details in the original image. A training system is also described herein for training the first and second neural networks. The training system alternates in the data it feeds the second neural network, first using a guidance image as input to the second neural network, and then using a corresponding ground-truth image.


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