Facial image processing apparatus, facial image processing method, and non-transitory computer-readable storage medium
First Claim
1. A facial image processing apparatus, comprising:
- a memory; and
one or more processor;
wherein the memory and the at least one processor are communicatively connected with each other;
the memory stores computer-executable instructions for controlling the one or more processors to;
automatically identify a facial feature from a facial image;
automatically extract an image portion defining the facial feature from the facial image;
perform a detail enhancement process on the image portion to obtain a detail-enhanced image portion corresponding to the image portion; and
perform an image composition process to compose the detail-enhanced image portion and the facial image to obtain an enhanced facial image;
wherein perform the image composition process comprises replace a portion of the facial image corresponding to the image portion with the detail-enhanced image portion to obtain an integrated facial image comprising the detail-enhanced image portion and a portion outside the detail-enhanced image portion integrated together; and
integrate the detail-enhanced image portion and the portion outside the detail-enhanced image portion, thereby obtaining the enhanced facial image;
wherein perform the detail enhancement process on the image portion to obtain the detail-enhanced image portion corresponding to the image portion comprises;
provide a deep neural network, the deep neural network being trained using facial image training data;
input an input signal comprising the image portion or derived from the image portion into an input layer of the deep neural network;
process the input signal through one or more hidden layers of the deep neural network to obtain a processed signal; and
output the processed signal from an output layer of the deep neural network as the detail-enhanced image portion;
wherein the deep neural network is trained using a plurality of pairs of high-resolution image portion and low-resolution image portion;
wherein the plurality of pairs of high-resolution image portion and low-resolution image portion are generated by;
providing a plurality of high-resolution reference image portions as an input; and
based on the plurality of high-resolution reference image portions, generating a plurality of low-resolution reference image portions respectively corresponding to the plurality of high-resolution reference image portions.
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Accused Products
Abstract
The present application discloses a facial image processing apparatus. The facial image processing apparatus includes a memory; and one or more processor. The memory and the at least one processor are communicatively connected with each other. The memory stores computer-executable instructions for controlling the one or more processors to automatically identify a facial feature from a facial image; automatically extract an image portion defining the facial feature from the facial image; perform a detail enhancement process on the image portion to obtain a detail-enhanced image portion corresponding to the image portion; and perform an image composition process to compose the detail-enhanced image portion and the facial image to obtain an enhanced facial image.
12 Citations
16 Claims
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1. A facial image processing apparatus, comprising:
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a memory; and one or more processor; wherein the memory and the at least one processor are communicatively connected with each other; the memory stores computer-executable instructions for controlling the one or more processors to; automatically identify a facial feature from a facial image; automatically extract an image portion defining the facial feature from the facial image; perform a detail enhancement process on the image portion to obtain a detail-enhanced image portion corresponding to the image portion; and perform an image composition process to compose the detail-enhanced image portion and the facial image to obtain an enhanced facial image; wherein perform the image composition process comprises replace a portion of the facial image corresponding to the image portion with the detail-enhanced image portion to obtain an integrated facial image comprising the detail-enhanced image portion and a portion outside the detail-enhanced image portion integrated together; and integrate the detail-enhanced image portion and the portion outside the detail-enhanced image portion, thereby obtaining the enhanced facial image; wherein perform the detail enhancement process on the image portion to obtain the detail-enhanced image portion corresponding to the image portion comprises; provide a deep neural network, the deep neural network being trained using facial image training data; input an input signal comprising the image portion or derived from the image portion into an input layer of the deep neural network; process the input signal through one or more hidden layers of the deep neural network to obtain a processed signal; and output the processed signal from an output layer of the deep neural network as the detail-enhanced image portion; wherein the deep neural network is trained using a plurality of pairs of high-resolution image portion and low-resolution image portion; wherein the plurality of pairs of high-resolution image portion and low-resolution image portion are generated by; providing a plurality of high-resolution reference image portions as an input; and based on the plurality of high-resolution reference image portions, generating a plurality of low-resolution reference image portions respectively corresponding to the plurality of high-resolution reference image portions. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A facial image processing method, comprising:
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automatically identifying a facial feature from a facial image; automatically extracting an image portion defining the facial feature from the facial image; performing a detail enhancement process on the image portion to obtain a detail-enhanced image portion corresponding to the image portion; and performing an image composition process to compose the detail-enhanced image portion and the facial image to obtain an enhanced facial image; wherein performing the detail enhancement process comprises replacing a portion of the facial image corresponding to the image portion with the detail-enhanced image portion to obtain an integrated facial image comprising the detail-enhanced image portion and a portion outside the detail-enhanced image portion integrated together; and
; andintegrating the detail-enhanced image portion and the portion outside the detail-enhanced image portion, thereby obtaining the enhanced facial image; wherein performing the detail enhancement process on the image portion comprises; providing a deep neural network, the deep neural network being trained using facial image training data; inputting an input signal comprising the image portion or derived from the image portion into an input layer of the deep neural network; processing the input signal through one or more hidden layers of the deep neural network to obtain a processed signal; and outputting the processed signal from an output layer of the deep neural network as the detail-enhanced image portion; the method further comprises training the deep neural network using facial image training data; wherein training the deep neural network comprises; providing a plurality of high-resolution reference image portions as an input; based on the plurality of high-resolution reference image portions, generating a plurality of low-resolution reference image portions respectively corresponding to the plurality of high-resolution reference image portions, thereby obtaining a plurality of pairs of high-resolution image portion and low-resolution image portion; and training the deep neural network using the plurality of pairs of high-resolution image portion and low-resolution image portion. - View Dependent Claims (10, 11, 12, 13, 14)
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15. A non-transitory computer-readable storage medium storing computer-readable instructions, the computer-readable instructions being executable by a processor to cause the processor to perform:
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automatically identifying a facial feature from a facial image; automatically extracting an image portion defining the facial feature from the facial image; performing a detail enhancement process on the image portion to obtain a detail-enhanced image portion corresponding to the image portion; and performing an image composition process to compose the detail-enhanced image portion and the facial image to obtain an enhanced facial image; wherein performing the detail enhancement process comprises replacing a portion of the facial image corresponding to the image portion with the detail-enhanced image portion to obtain an integrated facial image comprising the detail-enhanced image portion and a portion outside the detail-enhanced image portion integrated together; and
; andintegrating the detail-enhanced image portion and the portion outside the detail-enhanced image portion, thereby obtaining the enhanced facial image; wherein performing the detail enhancement process on the image portion comprises; providing a deep neural network, the deep neural network being trained using facial image training data; inputting an input signal comprising the image portion or derived from the image portion into an input layer of the deep neural network; processing the input signal through one or more hidden layers of the deep neural network to obtain a processed signal; and outputting the processed signal from an output layer of the deep neural network as the detail-enhanced image portion; wherein the deep neural network is trained by; providing a plurality of high-resolution reference image portions as an input; based on the plurality of high-resolution reference image portions, generating a plurality of low-resolution reference image portions respectively corresponding to the plurality of high-resolution reference image portions, thereby obtaining a plurality of pairs of high-resolution image portion and low-resolution image portion; and training the deep neural network using the plurality of pairs of high-resolution image portion and low-resolution image portion. - View Dependent Claims (16)
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Specification