Method for repairing face defect images based on auto-encoder and generative adversarial networks

A face image and self-encoder technology, applied in the field of image processing, can solve problems such as blurred image details, unsmooth images, complex image restoration and repair model design, and achieve the effect of improving clarity

Active Publication Date: 2018-09-11
XIANGTAN UNIV
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Problems solved by technology

[0008] In view of the complex design of image restoration and repair models in the prior art, the speed is slow, the efficiency is low, and it is easy to cause blurred ima...

Method used

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  • Method for repairing face defect images based on auto-encoder and generative adversarial networks
  • Method for repairing face defect images based on auto-encoder and generative adversarial networks
  • Method for repairing face defect images based on auto-encoder and generative adversarial networks

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Embodiment

[0058] Taking the CelebA face image dataset (178 pixels*218 pixels) as an example, when doing research on the restoration of the defect area of ​​the images in the CelebA face image dataset, we first need to select the training data set and the test data set, and combine them Perform preprocessing; use the processed data set to train the autoencoder model and the conditional generation network model respectively; then input the defective image into the trained autoencoder to obtain the filling content based on the information around the defect area; the filling content generated by the autoencoder The content is filled into the defective area of ​​the defective face image, and the obtained complete image input condition generates an adversarial network, so as to obtain a clear and natural restoration result. This example is the face defect image restoration process in the CelebA face image dataset.

[0059] The experimental environment is based on a GPU high-performance server...

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Abstract

The present invention provides a method for repairing face defect images based on an auto-encoder and generative adversarial networks. Through combination of the auto-encoder and the generative adversarial networks, the method comprises the following steps of: (1) performing face data set defect preprocessing; (2) employing the data set after processing to train the auto-encoder to allow the auto-encoder to reach the optimal state; (3) employing the data set after processing to train the condition generative adversarial networks to allow the condition generative adversarial networks to reach the optimal state; (4) inputting a defect image to be repaired into the trained encoder to generate a face image to be repaired; and (5) inputting the image to be repaired to the condition generative adversarial networks to generate much clearer and more natural restored face image. The method improves the restoring definition of the defect face area and the fidelity of the defect content, avoids the pseudomorphism of the defect area edges to the maximum extent, restricts the generation direction of the defect area and generates much clearer and more natural restoring effect.

Description

technical field [0001] The invention relates to a method for repairing a human face defect image, in particular to a method for repairing a human face defect image based on an autoencoder and a generated confrontation network, and belongs to the technical field of image processing. Background technique [0002] Face recognition technology has developed significantly in recent years. However, recognizing partially occluded faces is still a challenge for existing face recognition technologies. In real applications, there is an increasing demand for occluded image inpainting, such as surveillance and security. Image restoration, as a common image editing operation, aims to fill missing or masked areas in an image with plausible content. The resulting content can be as accurate as the original or completely conform to the overall image, making the restored image look real. Due to the inherent ambiguity and complexity of natural images, image restoration (image filling) has be...

Claims

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Application Information

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IPC IPC(8): G06T5/00G06K9/00G06N3/04
CPCG06T5/001G06V40/161G06V40/168G06V40/172G06N3/045
Inventor 唐欢容刘恋欧阳建权
Owner XIANGTAN UNIV
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