High-resolution image generation method based on generative adversarial network

Abstract

The invention discloses a high-resolution image generation method based on a generative adversarial network. The method comprises the following steps: firstly, preprocessing a to-be-learned data set image to obtain a training set; constructing a generative adversarial network comprising a generative network and a discrimination network; pre-training the generative adversarial network; obtaining pre-trained model parameters as initialization parameters of the generative adversarial network; then, separately inputting the training set and an image generated by the generative network into the discrimination network, enabling the output of the discrimination network to react on the generative network, carrying out adversarial training on the generative adversarial network, optimizing network parameters of the generative network and the discrimination network, and ending the training when a loss function converges to obtain a trained generative adversarial network; and finally, inputting the random data distribution into the trained generation network to realize high-resolution image generation. According to the invention, the generated image is clearer, the training process is stable,and the network converges quickly.

Description

technical field [0001] The invention relates to the fields of deep learning and image processing, in particular to a method for generating high-resolution images based on generating confrontation networks. Background technique [0002] With the development of graphics rendering technology, digital signal processing technology, sensing technology, and graphics technology, the research on virtual reality is becoming more and more extensive, that is, using computers to generate realistic images, etc., so that users can interact and control in the virtual environment. In terms of content creation and intelligent editing, many software can change the expression, wrinkles, etc. of images, which puts great demands on high-quality and diverse image generation technologies. At the same time, in many companies (such as credit card companies) that have high requirements for information security, it is necessary to construct virtual fraud data, images, etc. to improve the fraud detectio...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is that the detailed effect of LAPGAN in generating images in the prior art is blurred, the training method of the network is too free, the model lacks constraints, it is difficult to balance all levels of GAN, and for larger input pictures or more pixels Defects or problems that will cause training or mode collapse and make the entire model uncontrollable, provide a high-resolution image generation method based on generative confrontation network, to achieve clearer generated images, stable training process and faster network convergence

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