Deep learning model for noise reduction in low SNR imaging conditions
AssigneeNanotronics Imaging, Inc.
InventorsDenis Sharoukhov, Tonislav Ivanov, Jonathan Lee
Life of this patent
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Abstract
Embodiments disclosed herein are generally related to a system for noise reduction in low signal to noise ratio imaging conditions. A computing system obtains a set of images of a specimen. The set of images includes at least two images of the specimen. The computing system inputs the set of images of the specimen into a trained denoising model. The trained denoising model is configured to output a single denoised image of the specimen. The computing system receives, as output from the trained denoising model, a single denoised image of the specimen.
Claims20
A system, comprising:
- an imaging apparatus configured to perform darkfield imaging of a specimen;
- and
- a computing system in communication with the imaging apparatus, the computing system comprising one or more processors and a memory, the memory having programming coded thereon, which, when executed by the one or more processors, causes the computing system to perform operations comprising:
- obtaining, by the computing system, a plurality of noisy images of the specimen captured by the imaging apparatus using darkfield imaging, wherein the plurality of noisy images includes at least two images of the specimen captured using darkfield imaging;
- denoising, by the computing system, the plurality of noisy images by inputting the plurality of noisy images of the specimen into a convolutional neural network trained to output a single denoised image of the specimen;
- and
- generating, as output from the convolutional neural network, the single denoised image of the specimen.
A method comprising:
- obtaining, by a computing system, a plurality of noisy images of a specimen captured by an imaging apparatus using darkfield imaging, wherein the plurality of noisy images includes at least two images of the specimen captured using darkfield imaging;
- denoising, by the computing system, the plurality of noisy images by inputting the plurality of noisy images of the specimen into a convolutional neural network trained to output a single denoised image of the specimen;
- and
- generating, as output from the convolutional neural network, the single denoised image of the specimen.
A non-transitory computer readable medium comprising one or more sequences of instructions, which, when executed by one or more processors, causes a computing system to perform operations, comprising:
- obtaining, by the computing system, a plurality of noisy images of a specimen captured by an imaging apparatus using darkfield imaging, wherein the plurality of noisy images includes at least two images of the specimen captured using darkfield imaging;
- denoising, by the computing system, the plurality of noisy images by inputting the plurality of noisy images of the specimen into a convolutional neural network trained to output a single denoised image of the specimen;
- and
- generating, as output from the convolutional neural network, the single denoised image of the specimen.
Dependencies are inferred from the claim text.