An image complement method based on a generated antagonistic network model

Abstract

The invention discloses an image complement method based on a generated antagonistic network model, and belongs to the field of depth learning neural network, comprising the following steps: S1, constructing an original generated antagonistic network model; 2, constructing a depth convolution neural network as a generator and a discriminator; S3, removing part of pixels in the data set image and inputting them into the generator; S4, using convolution neural network to complete the image in the generator; S5, inputting the completed image and the dataset image into a discriminator to discriminate, and updating the loss function. In this method, the generated antagonistic network model based on image complement is constructed, which changes the information received by the generator from noise to image with partial pixels removed. Through antagonistic training between the generator and the discriminator, the generator can automatically complete the missing partial pixels.

Description

technical field [0001] The invention relates to the technical field of deep learning neural network, in particular to an image completion method based on a generative confrontation network model. Background technique [0002] Generative Adversarial Network (GAN for short) is a deep learning framework proposed by Goodfellow in 2014. It is based on the idea of ​​"game theory" and constructs two models, the generator and the discriminator. The former The image is generated by inputting (0, 1) uniform noise or Gaussian random noise, which discriminates the input image to determine whether it is an image from the dataset or an image produced by the generator. [0003] In the traditional confrontational network model, the discriminator receives random noise, and by continuously learning the distribution in the data set, the random noise is generated into an image that satisfies the distribution of the data set. In this case, the whole network is slow to train and there is no auto...

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Problems solved by technology

In this case, the whole network is slower to train and there is no autocompletion of images

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