Multi-domain image conversion method, system and medium based on conditional generative confrontation network

Abstract

The present invention relates to the field of deep learning image generation, and in particular to a multi-domain image conversion method, system and medium based on conditional generative confrontation networks. Original image y; use a pre-trained condition extractor to produce x modal condition C x and the y-mode condition C y ;The original image x, original image y, x modal condition C x , y modality inputs the pre-trained conditional generative adversarial network to obtain the corresponding image conversion result. The present invention uses a feature extractor to extract the features of the original image, obtains a conditional matrix through upsampling and splicing with the zero matrix on the channel, and retains the semantic information of each modal input in the case of high independence; The invention is flexible in training, has no limit to the number of domains to be converted, and requires few parameters.

Description

technical field [0001] The invention relates to the field of deep learning image generation, in particular to a conditional generative confrontation network-based multi-domain image conversion method, system and medium, especially suitable for image style transfer and multi-modal conversion of medical images. Background technique [0002] Convolutional neural network is an important research direction in the field of deep learning (DL, Deep Learning), and has now become part of the most influential innovation in the field of computer vision. The main feature of the convolutional neural network is the convolution operation, which is good at matrix operations, and the channel of the image can generally be expressed as a two-dimensional matrix, so the convolutional neural network performs excellently on image-related tasks. [0003] Since the Generative Adversarial Network (GAN) was proposed in 2014, the image neighborhood has made great progress, many in classification, segmen...

AI Technical Summary

Problems solved by technology

[0004] We know that classic GAN can only generate random pictures, but not specified pictures

For example, if we want to use GAN to generate a picture of a car of a specified color, GAN can only ensure that the generated picture is a car, but cannot guarantee that the generated car must be of the color we specified

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