Neural Network Image Enhancement
AssigneesHewlett-Packard Development Company, L.P., Purdue Research Foundation
InventorsXiaoyu Xiang, Qian Lin, Jan Allebach, Tianqi Guo
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Abstract
In some examples, a computing device can include a processor resource and a non-transitory memory resource storing machine-readable instructions stored thereon that, when executed, cause the processor resource to identify a base resolution of a captured image having a base image quality, perform, via an individual neural network, neural network calculations on the captured image to form an enhanced image having an resolution that is higher than the base resolution and image quality that is higher than the base image quality.
Claims15
A computing device, comprising:
- a processor resource;
- and
- a non-transitory memory resource storing machine-readable instructions stored thereon that, when executed, cause the processor resource to:
- identify a base resolution of a captured image having a base image quality;
- and
- perform, via an individual neural network, a plurality of neural network calculations on the captured image to form an enhanced image having:
- an increased resolution that is higher than the base resolution;
- and
- an increased image quality that is higher than the base image quality.
A non-transitory memory resource storing machine-readable instructions stored thereon that, when executed, cause a processor resource to:
- identify a base resolution of a captured image having a base image quality;
- and
- perform, via an individual convolutional neural network (CNN), a plurality of CNN calculations on the captured image to form an enhanced image having an increased resolution that is higher than the base resolution and an increased image quality that is higher than the base resolution, the CNN calculations including CNN calculations to:
- segment the captured image into image dimensions having a given height and a given width;
- pool the image dimensions to form pooled image dimensions;
- extract features from the pooled image dimensions;
- and
- perform subpixel convolution based on the extracted features and the pooled image dimension to form the enhanced image.
A computing device, comprising:
- an image capture device to capture an image with a base resolution and a base image quality;
- and
- a processor resource to:
- receive image data of an image from the image capture device;
- and
- perform, via an individual convolutional neural network (CNN), a plurality of CNN calculations on the captured image to form an enhanced image having an increased resolution that is higher than the base resolution and an increased image quality that is higher than the base resolution, the CNN calculations including CNN calculations to:
- segment the image data into image dimensions having a given height and a given width;
- pool the image dimensions to form pooled image dimensions;
- extract features from the pooled image dimensions;
- and
- perform subpixel convolution based on the extracted features and the pooled image dimension to form the enhanced image.
Dependencies are inferred from the claim text.