US 12,277,683 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. 17/445,376.
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.
Claims priority of provisional application 63/163,682, filed on Mar. 19, 2021.
Prior Publication US 2022/0301112 A1, Sep. 22, 2022
Int. Cl. G06T 5/70 (2024.01); G06N 3/04 (2023.01); G06T 5/50 (2006.01); G06V 20/69 (2022.01)
CPC G06T 5/70 (2024.01) [G06N 3/04 (2013.01); G06T 5/50 (2013.01); G06V 20/695 (2022.01)] 33 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 convolutional layer configured to convolve and linearly rectify the image and the at least one other image of the sequence of images to generate a first output;
a plurality of layers, wherein individual ones of the plurality of layers are configured to receive a memory output of a previous layer of the plurality of layers and provide, as an output, a portion of noise of the image, wherein a first layer of the plurality of layers is configured to receive the first output from the convolutional layer and wherein the machine learning model is configured to remove from the image the portion of noise provided by the previous layer of the plurality of layers to generate an intermediate image at each layer of the plurality of layers and provide the intermediate image to each next layer of the plurality of layers; and
a second convolutional layer configured to convolve an output of a last one of the plurality of layers and output a final portion of the noise of the image, wherein the machine learning model is configured to remove the final portion of the noise from the image.