Face image repairing method based on generative adversarial network

A face image and repair method technology, applied in the field of image processing, can solve problems such as limited image repair application scenarios, and achieve the real effect of face images

Active Publication Date: 2018-04-20
NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

[0003] Traditional image restoration techniques often require specific shapes of defect part

Method used

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  • Face image repairing method based on generative adversarial network
  • Face image repairing method based on generative adversarial network
  • Face image repairing method based on generative adversarial network

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Embodiment Construction

[0044] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0045] Such as figure 1 As shown, the present invention designs a face image restoration method based on a generative confrontation network. In practical applications, it specifically includes the following steps:

[0046] Step A. Collect a large number of images containing complete and clear faces through the existing database or from the Internet, and use them to train the generative confrontation network. OpenFace is a face detection method based on deep neural network. Using this method, the face part in each image is intercepted, and the scale is normalized into a 64×64 pixel image, which is named sequentially by number and saved in the same file. folder, thus constructing a face image database containing 6400 images.

[0047] Step B. Build a generative confrontation network model, including a generator G and a disc...

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Abstract

The invention discloses a face image repairing method based on a generative adversarial network. The method comprises the following steps that: (1) searching a great quantity of images which contain complete and clear faces, and establishing a face image database; (2) constructing the generative adversarial network; (3) training the generative adversarial network, and optimizing the parameters ofa generator and a descriminator in the generative adversarial network; and (4) inputting random vectors which obey normal distribution into the trained generator, generating the face image, comparingthe intact area of the face image to be repaired with the corresponding area of the generated image, continuously regulating the input vectors until the intact area of the face image to be repaired and the corresponding area of the generated image are similar, and finally, replacing the pixel value of a blocked or damaged area in the face image to be repaired by the pixel value of the corresponding area for generating the face image. By use of the method, the generative adversarial network with a deep learning structure is adopted by aiming at the problem that the blocked or damaged face imageis repaired, and therefore, an image repairing problem in image processing is effectively 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 generative confrontation network. Background technique [0002] With the popularity of electronic photographic equipment, digital photos have entered every aspect of people's lives, and image processing has therefore received extensive attention. Image inpainting is an extremely important part of image processing. Image restoration uses the information of the intact part of the image to fill in the occluded, damaged or redundant parts. It can be used to remove the occlusion of photos, repair damaged cultural relic images, image data preprocessing and other fields. [0003] Traditional image inpainting techniques often require specific shapes of defect parts and simple texture repetition, which limits the application scenarios of image inpainting. With the improvement of computer computing power and the maturity of...

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Application Information

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IPC IPC(8): G06T5/00G06T3/40
CPCG06T3/40G06T5/005G06T2207/20081G06T2207/30201
Inventor 卢官明郝强刘华明毕学慧
Owner NANJING UNIV OF POSTS & TELECOMM
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