Research On Image Style Transfer Method Based On Deep Learning

Image style transfer algorithm is a research hotspot of computer vision.It is the process of applying the style of one style image to another content image,which is an artistic creation and image editing technique.In recent years,the rapid development of deep learning has injected new impetus into the field of computer vision,and a large number of image style transfer algorithms based on deep learning have been proposed.This paper designs two image style transfer algorithms based on neural networks.First,from the perspective of improving the quality of stylized images,an image style transfer algorithm with salient area preservation is proposed;then,from the perspective of improving the efficiency of style transfer,a lightweight image style transfer algorithm that introduces an attention mechanism is proposed.The work of this paper is as follows:1.The relevant research background and research significance of the image style transfer method are studied,and the time sequence of its development is summarized;the style transfer algorithm based on deep learning is summarized,and the advantages and disadvantages of each type of algorithm are analyzed.2.Propose an image style transfer algorithm for salient area preservation.Aiming at the problem that the existing style transfer algorithm will cause the salient area of the content image to be distorted during the stylization process,this paper proposes an image style transfer algorithm with salient area preservation.Based on the fast style transfer algorithm,this method adds a saliency detection network and designs a saliency loss function.In the training process,the difference between the saliency map of the generated image and the content image is additionally calculated,and the saliency loss is used as a part of the total loss for iterative training.Experiments show that the algorithm generated by the stylized image can better retain the salient area of the content image,and has a good visual effect.3.Propose a lightweight image style transfer algorithm with attention mechanism.From the perspective of improving operating efficiency,this paper proposes a lightweight image style transfer algorithm with attention mechanism.This method uses a lightweight convolutional neural network as the main network for style transfer,and introduces an attention mechanism.Compared with the original network,the parameter amount is very small,and the performance of the neural network can be improved with almost no additional burden.Experiments prove that the lightweight style transfer algorithm proposed in this paper can not only guarantee the quality of stylized images,but also the network model has fewer parameters and smaller size,and is easy to deploy on mobile devices.