Face super-resolution method and system based on dual identity attribute constraints

An Identity-attributed, Super-Resolution Technology

Active Publication Date: 2021-06-11
WUHAN INSTITUTE OF TECHNOLOGY
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Problems solved by technology

[0005] In order to solve the above technical problems, the present invention provides a face super-resolution method and system based on dual identity attribute constraints, which solves the many-to-one problem in the downgrading process and the resulting identity information of LR facial images cannot be retained after downgrading problems that limit image reconstruction performance

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  • Face super-resolution method and system based on dual identity attribute constraints
  • Face super-resolution method and system based on dual identity attribute constraints
  • Face super-resolution method and system based on dual identity attribute constraints

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[0045] The present invention will be further described below in conjunction with accompanying drawing:

[0046] The present invention proposes a face super-resolution method and system based on dual identity attribute constraints. The face image super-resolution method introduces double-loop constraints, one cycle starts from a low-resolution image to generate a high-resolution image, and the other One cycle starts with high-resolution images for the learning degradation process. Furthermore, using dual identities to constrain images from both high-resolution and low-resolution feature spaces improves facial feature rendering.

[0047] figure 1 It is a schematic flowchart of a face super-resolution method based on dual identity attribute constraints provided by an embodiment of the present invention. figure 2 It is an overall training scheme of a face super-resolution method based on dual identity attribute constraints proposed by the present invention. Through generator G,...

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Abstract

The invention discloses a face super-resolution method and system based on dual identity attribute constraints. The method comprises the following steps: S1, acquiring a corresponding low-resolution face image LR and a corresponding high-resolution face image HR; s2, inputting the LR into a first generator to obtain a face image SR in a high-resolution space, and inputting the SR into a second generator to obtain a face image LR'in a low-resolution space; s3, inputting the HR into a second generator to obtain a face image LR ''in a low-resolution space, and inputting the LR'' into the first generator to obtain a face image SR 'in a high-resolution space; s4, performing forward closed-loop constraint on LR and LR ', and performing reverse closed-loop constraint on HR and SR'; and S5, performing forward identity constraint on the SR and the SR', and performing reverse identity constraint on the LR'' and the LR'. The invention provides a dual closed-loop network with double identity attributes, which can super-distinguish a low-resolution facial image to a corresponding high-resolution part and reserve identity information at the same time, and can effectively improve the super-resolution reconstruction performance of the facial image.

Description

technical field [0001] The invention belongs to the technical field of super-resolution, and in particular relates to a face super-resolution method and system based on dual identity attribute constraints. Background technique [0002] Face super-resolution (Super-Resolution, SR) is a technique to reconstruct a potential high-resolution (High Resolution, HR) face image from an input low-resolution (Low Resolution, LR) face image, which is An important image processing method to improve the resolution of images and videos in computer vision, which has been widely used in surveillance, satellite remote sensing, face recognition and other fields. Compared with general SR tasks, face image SR is more challenging due to its severe morbidity. [0003] Face SR methods use face prior knowledge to super-resolve LR faces, learn subspaces from LR and HR face images, and then reconstruct the HR output from the Principal Component Analysis (PCA) coefficients of the LR input. Markov Ran...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T3/40
CPCG06T3/4053G06T3/4046
Inventor 卢涛程芳芳张彦铎吴云韬
Owner WUHAN INSTITUTE OF TECHNOLOGY
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