US 10,593,021 B1Granted

Motion deblurring using neural network architectures

AssigneeInception Institute of Artificial Intelligence, Ltd.

InventorsJianbing Shen, Ziyi Shen, Wenguan Wang, Xiankai Lu, Ling Shao

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Abstract

This disclosure relates to improved techniques for performing computer vision functions including motion deblurring functions. The techniques described herein utilize a neural network architecture to perform these functions. The neural network architecture can include a human-aware attention model that is able to distinguish between foreground human objects and background portions of degraded images affected by motion blur. The neural network architecture further includes an encoder-decoder network that separately performs motion deblurring functions on foreground and background portions of degraded images, and reconstructs enhanced images corresponding to the degraded images.

Claims20

Dependent claims

A system for generating an enhanced image from a degraded image comprising:

one or more computing devices comprising one or more processors and one or more non-transitory storage devices for storing instructions, wherein execution of the instructions by the one or more processors causes the one or more computing devices to:
receive, at a neural network architecture, a degraded image that includes motion blur, the degraded image comprising a foreground portion and a background portion;
analyze, using a human-aware attention model associated with the neural network architecture, the degraded image to generate one or more human-aware attention masks;
generate, using an encoder associated with the neural network architecture, an encoded representation of the degraded image;
generate, using the encoded representation and the one or more human-aware attention masks, foreground feature information and background feature information corresponding to the degraded image;
receive, at a multi-branch decoder of the neural network architecture, the foreground feature information and the background feature information;
separately perform, with the multi-branch decoder of the neural network architecture, deblurring functions on the foreground portion and the background portion of the degraded image using the foreground feature information and the background feature information;
and
reconstruct an enhanced image corresponding to the degraded image.

6 more elements

A method for generating an enhanced image from a degraded image comprising:

receiving, at a neural network architecture, a degraded image that includes motion blur, the degraded image comprising a foreground portion and a background portion;
analyzing, using a human-aware attention model associated with the neural network architecture, the degraded image to generate one or more human-aware attention masks;
generating, using an encoder associated with the neural network architecture, an encoded representation of the degraded image;
generating, using the encoded representation and the one or more human-aware attention masks, foreground feature information and background feature information corresponding to the degraded image;
receiving, at a multi-branch decoder of the neural network architecture, the foreground feature information and the background feature information;
separately performing, with the multi-branch decoder of the neural network architecture, deblurring functions on the foreground portion and the background portion of the degraded image using the foreground feature information and the background feature information;
and
reconstructing an enhanced image corresponding to the degraded image.

5 more elements

Dependencies are inferred from the claim text.