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. 06, 2026

Filed:

Oct. 28, 2022
Applicant:

GE Precision Healthcare Llc, Wauwatosa, WI (US);

Inventors:

Michel Souheil Tohme, Wauwatosa, WI (US);

German Guillermo Vera Gonzalez, Menomonee Falls, WI (US);

Ludovic Boilevin Kayl, Versailles, FR;

Vincent Bismuth, Paris, FR;

Tao Tan, Nuenen, NL;

Assignee:

GE Precision Healthcare, LLC, Wauwatosa, WI (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 5/70 (2024.01); A61B 6/00 (2024.01); G06T 5/20 (2006.01); G06T 7/00 (2017.01);
U.S. Cl.
CPC ...
G06T 5/70 (2024.01); G06T 5/20 (2013.01); G06T 7/0014 (2013.01); A61B 6/5258 (2013.01); G06T 2200/24 (2013.01); G06T 2207/10116 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30061 (2013.01);
Abstract

Various methods and systems are provided for training a denoising system for a digital imaging system. The denoising system can be a deep learning denoising system formed as a blind or non-blind denoising system in which the training dataset provided to the denoising system includes a noisy image formed with simulated noise added to a clean digital image, and a reference image formed of the clean image having residual noise added thereto, where the residual noise is a fraction of the simulated noise used to form the noisy image. The use of the residual noise within the reference image of the training dataset teaches the DL network in the training process to remove less than all the noise during subsequent inferencing of digital images from the digital imaging system. By leaving selected amounts of noise in the digital images, the denoiser can be tuned to improve image attributes and texture.


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