Face image restoration method based on dense expansion convolution self-coding adversarial network

A technology of convolutional self-encoding and face image, applied in the field of face image restoration based on dense dilated convolutional self-encoding confrontation network, can solve the problems of artifacts, poor visual similarity, blurred image restoration, etc., to achieve consistent improvement performance, improved stability, increased representational power and robustness

Active Publication Date: 2020-01-14
BEIJING UNIV OF TECH
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The learning-based inpainting method can repair images with missing semantic information by learning a large amount of imag

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  • Face image restoration method based on dense expansion convolution self-coding adversarial network
  • Face image restoration method based on dense expansion convolution self-coding adversarial network
  • Face image restoration method based on dense expansion convolution self-coding adversarial network

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[0057] In order to make the purpose of the method of the present invention, technical solutions and advantages more clear, the present invention is explained below in conjunction with the accompanying drawings and examples, and is not intended to limit the present invention:

[0058] as attached figure 1 As shown, the face image restoration method based on dense dilated convolutional self-encoding confrontation network includes the following steps:

[0059] Step 1. Perform face recognition on the public face data set, extract key information of the face, cut out the face image with the background removed, and scale its size to 128*128 to obtain the face data set.

[0060] Step 2. Construct a densely expanded convolutional self-encoder confrontation network. The network is as follows figure 2 As shown, it contains two parts: the generation network and the discriminative network.

[0061] (1) The generation network includes an encoding layer, a connection layer, and a decodin...

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Abstract

The invention discloses a face image restoration method based on a dense expansion convolution self-coding adversarial network. The method comprises the following steps: firstly, preprocessing face public data to obtain a face data set; secondly, constructing a dense expansion convolution self-coding adversarial network; then pre-training a dense expansion convolutional self-encoding generation network by utilizing reconstruction loss, and alternately carrying out the following training steps: (1) training a double-discrimination network by utilizing adversarial loss; (2) training a pre-trained generative network by using joint loss; and then obtaining a trained dense expansion convolution self-encoding generation network, finally inputting the image to be restored into the generation network, and fusing the generation image and the defect image to obtain a final restored image. According to the method, the face image restoration problem of serious semantic information loss and large-area random region loss is solved.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a face image restoration method based on a densely expanded convolutional self-encoding confrontation network. Background technique [0002] Image restoration refers to the use of computer technology to automatically estimate the information of the damaged area of ​​the image and fill in the restored image. In the process of image collection, transmission, storage, etc., there will be many reasons to destroy the integrity of the image information, and the face image not only contains the identity information of the person, but also contains a wealth of important information such as the expression and psychological activities of the person. has a wide range of applications. How to effectively repair the damaged face image is particularly important for face recognition. [0003] Existing image inpainting methods can be divided into two categories: learning ty...

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Application Information

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IPC IPC(8): G06T5/00G06K9/00G06K9/62G06N3/04
CPCG06T5/005G06V40/172G06N3/045G06F18/214Y02T10/40
Inventor 任坤范春奇黄泷
Owner BEIJING UNIV OF TECH
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