Studies Advanced in Image Style Transfer based on Deep Learning
DOI:
https://doi.org/10.54097/hset.v39i.6756Keywords:
Image Style Transfer; Loop Always Adversarial Network; Generative Adversarial Network.Abstract
A popular project in computer vision in recent years is called "image transfer", which attempts to create new images containing features from abstract properties of the content and style of a given image. Convolutional neural networks have generated the basic components of image style transfer, significantly improving transfer accuracy and visualization effect, thanks to deep learning's quick progress. The purpose of this study is to highlight the most recent developments in the area of deep learning-based picture style transfer. We first provide a detailed introduction to the traditional style transfer algorithms from the frameworks of generative adversarial networks and convolutional neural networks., including the design ideas research issues, key steps, advantages and disadvantages. On a few application tasks, we qualitatively compare the visualization outcomes of various approaches, and we talk about the subject's current issues and potential future developments.
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