Low-light image selection for neural network training

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United States of America Patent

PATENT NO 11899753
APP PUB NO 20220366189A1
SERIAL NO

17317480

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Abstract

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This disclosure provides methods, devices, and systems for low-light imaging. The present implementations more specifically relate to selecting images that can be used for training a neural network to infer denoised representations of images captured in low light conditions. In some aspects, a machine learning system may obtain a series of images of a given scene, where each of the images is associated with a different SNR (representing a unique combination of exposure and gain settings). The machine learning system may identify a number of saturated pixels in each image and classify each of the images as a saturated image or a non-saturated image based on the number of saturated pixels. The machine learning system may then select the non-saturated image with the highest SNR as the ground truth image, and the non-saturated images with lower SNRs as the input images, to be used for training the neural network.

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Patent Owner(s)

Patent OwnerAddress
SYNAPTICS INCORPORATED1109 MCKAY DRIVE SAN JOSE CA 95131

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Inventor(s)

Inventor Name Address # of filed Patents Total Citations
Hargreaves, Kirk Mountain View, US 58 1945
Oreifej, Omar Campbell, US 8 50
Shanmuga, Vadivel Karthikeyan San Jose, US 6 7
Worfolk, Patrick A San Jose, US 19 629

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