Unsupervised image style migration method based on dual learning

Abstract

The invention relates to an unsupervised image style migration method based on dual learning, and belongs to the field of computer vision. The method comprises the following steps: firstly, preprocessing training data, and then designing network structures of a generator and a discriminator; designing a loss function and training the generator and the discriminator by using the training data and the loss function to obtain an unsupervised image style migration network ST; introducing an aesthetic scoring model to maximize the aesthetic quality score of the generated image; taking basic pixel features and advanced semantic features of the images as dual consistency constraints of unsupervised training, and dynamically adjusting the weights of the two features; adaptively adjusting convergence rates of the model in different style migration directions by using a style balance technology; and finally, performing style migration on the input image by applying ST. Compared with an existingmethod, the method has the advantages that the target image with higher quality can be generated, good universality is achieved, meanwhile, the training process of the model is more stable, and selection and design of the network structure are more flexible.

Description

technical field [0001] The invention designs an unsupervised image style transfer method based on dual learning, and in particular relates to a method based on a so-called generative confrontation network and using multiple loss function training for unsupervised image style transfer, which belongs to the field of computer vision technology. Background technique [0002] With the deepening of the era of artificial intelligence, a large number of image applications have sprung up like mushrooms, one of which is a variety of image beautification apps that focus on filter functions, and the key technology of filter functions is image style transfer. [0003] Image style transfer refers to converting the original image into another style of image while keeping the main content of the image unchanged, such as the conversion of landscapes in different seasons, the conversion of different painting styles, etc. Unsupervised image style transfer based on neural networks means that th...

AI Technical Summary

Problems solved by technology

However, the method based solely on the confrontational network to obtain the style transfer image often has shortcomings such as more noise and local distortion.

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