US 12,148,125 B2
Modular machine learning models for denoising images and systems and methods for using same
Bambi L DeLaRosa, Boise, ID (US); Katya Giannios, Boise, ID (US); and Abhishek Chaurasia, Boise, ID (US)
Assigned to Micron Technology, Inc., Boise, ID (US)
Filed by MICRON TECHNOLOGY, INC., Boise, ID (US)
Filed on Aug. 18, 2021, as Appl. No. 17/445,380.
Claims priority of provisional application 63/163,682, filed on Mar. 19, 2021.
Claims priority of provisional application 63/163,688, filed on Mar. 19, 2021.
Claims priority of provisional application 63/163,678, filed on Mar. 19, 2021.
Prior Publication US 2022/0301113 A1, Sep. 22, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06T 5/50 (2006.01); G06N 20/00 (2019.01); G06T 5/70 (2024.01)
CPC G06T 5/70 (2024.01) [G06N 20/00 (2019.01); G06T 5/50 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20216 (2013.01)] 37 Claims
OG exemplary drawing
 
1. A system comprising:
at least one processor; and
at least one non-transitory medium accessible to the processor, the at least one non-transitory medium encoded with instructions that, when executed, cause the system to implement a machine learning model, wherein the machine learning model is configured to identify at least some noise of an image of a sequence of images based, at least in part, on the image and at least one other image of the sequence of images, wherein the machine learning model comprises:
a plurality of layers, wherein individual ones of the plurality of layers are configured to provide a portion of noise of the image based, at least in part, on a first output and a second output provided by a previous layer of the plurality of layers, wherein the first output is based, at least in part on the at least one other image and the second output is based, at least in part, on the image.