Micron Technology Incorporated

Katya Giannios

Abhishek Chaurasia

Bambi L DeLaRosa

In some examples, a machine learning model may be trained to denoise an image. In some examples, the machine learning model may identify noise in an image of a sequence based at least in part, on at least one other image of the sequence. In some examples, the machine learning model may include a recurrent neural network. In some examples, the machine learning model may have a modular architecture including one or more building units. In some examples, the machine learning model may have a multi-branch architecture. In some examples, the noise may be identified and removed from the image by an iterative process.

Systems and methods for training machine learning models for denoising images

A machine learning model may be trained to denoise an image. The machine learning model may identify noise in an image of a sequence based at least in part, on at least one other image of the sequence. The machine learning model may include a recurrent neural network. The machine learning model may have a modular architecture including one or more building units. The machine learning model may have a multi-branch architecture. The noise may be identified and removed from the image by an iterative process.

Modular machine learning models for denoising images and systems and methods for using same

Building units for machine learning models for denoising images and systems and methods for using same

A system, method and apparatus of image segmentation with semi supervised training of an artificial neural network using coarse labels. For example, a first artificial neural network is trained to perform image segmentation on first images according to fine labels of image segmentation for the first images and to perform image segmentation on second images according to coarse labels of image segmentation for the second images. After the training, the first artificial neural network is used to perform image segmentation of the second images to identify improved labels of image segmentation for the second images. Subsequently, a supervised machine learning technique can be used to train a second artificial neural network to perform image segmentation on the first images according to fine labels and on the second images according to the improved labels.

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Boise, ID, United States of America

Katya Giannios