Face data pair-oriented complete face reticulate pattern restoration method and system and medium

A repair method and repair system technology, applied in the field of facial texture repair, can solve the problems of missing image information, repairing image distortion, affecting face repair tasks, etc., achieving high quality and avoiding information loss.

Active Publication Date: 2020-09-01
CENT SOUTH UNIV
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Problems solved by technology

If this multi-task facial texture repair method does not segment the precise texture in the first step, it will affect the subsequent texture facial restoration task, and there will be obvious repair traces.
In a separate segmentation or repair task, it is necessary to convert the size of the image...

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  • Face data pair-oriented complete face reticulate pattern restoration method and system and medium
  • Face data pair-oriented complete face reticulate pattern restoration method and system and medium
  • Face data pair-oriented complete face reticulate pattern restoration method and system and medium

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Embodiment Construction

[0039] The GAN network used in the embodiment of the present invention is a Feature Constraint Generative Adversarial Networks (FCGAN for short) that generates realistic images based on pixel-level semantics.

[0040] FCGAN is a GAN network, and the purpose of the discriminant network is to distinguish real samples y~p data and generate samples G(z). On the contrary, the generation network G tries to confuse the discriminative network D by generating samples, so that the pictures generated by the generation network become more and more realistic. Therefore, the present invention also adopts GANs learning strategy to solve the image-to-image translation task. Such as figure 1 As shown, the image generation network G is used to generate a given input image Each different input noise image x will correspond to a target image y, the present invention assumes that all target images obey the distribution p real , and change it to encourage the image G(x) to have the same di...

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Abstract

The invention discloses a face data pair-oriented complete face reticulate pattern restoration method and system and a medium. The method comprises the following steps of combindinga face reticulate pattern image and a clean face image corresponding to the face reticulate pattern image into a picture in the horizontal dimension; taking a training set composed of the data pairs as input of a GAN network, and training to obtain a restoration model; and inputting a to-be-restored face reticulate pattern image into the restoration model to obtain a restored clean face image. The method is an end-to-end training mode, the step of reticulate pattern recognition is omitted, and the bottleneck of reticulate pattern segmentation-based restoration is broken through. The image with the reticulate pattern is used as noise data, the face image without the reticulate pattern can be directly generated through end-to-end adversarial training, the integrity of the restored face reticulate pattern imageis improved while the face reticulate pattern restoration process is simplified, and obvious restoration traces cannot appear.

Description

technical field [0001] The invention relates to the field of face image processing, in particular to a method, system and medium for face texture restoration for complete face data. Background technique [0002] The existing image style conversion algorithms all need to be initialized, and the parameters of the CNN need to be fixed before backpropagating to update the image, which has poor performance. [0003] The Pix2pix network has a good performance when doing image restoration. Use the clean face and face texture data as the input data pair, where the face texture is equivalent to the noise data, the clean face is equivalent to the target image, and the image generated by the generation network is the output image. The Pix2pix generation network uses the U-net structure with Encoder-Decoder, which allows each deconvolution layer to have the features extracted by the downsampling convolution layer, so that it can carry more information. Its discriminative network also ...

Claims

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Application Information

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IPC IPC(8): G06T7/11G06T7/40G06T7/90G06T5/00G06T3/40G06N3/08G06N3/04
CPCG06T7/11G06T7/40G06T5/005G06T3/4053G06N3/08G06T7/90G06N3/045
Inventor 邝砾王胤朱雨佳
Owner CENT SOUTH UNIV
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