# Coarse-to-Fine Structure-Aware Artistic Style Transfer
## 2 Related Work  
### Style Transfer  
The goal of style transfer is to combine the texture of a style image with the structure of a content image. [PERSON] et al. [1] proposed a seminal iterative method that was based on a pretrained visual geometry group (VGG) network [7]. In this method, the content structure and the style texture can be used to synthesize a new image, but it is expensive, and a stylized image is generated only after the training process has been completed. Inspired by [PERSON] et al. [1], [PERSON] et al. [3] proposed a feed-forward method, which can be used to synthesize arbitrary images with a fixed style by an encoder-decoder architecture; the time and computation costs are reduced when using this method. Numerous methods have been developed to speed up the style transfer process [4; 8] and improve the visual quality [9; 10; 11]. [PERSON] et al. [12] also improved the stylization quality by proposing a style-aware loss, but they trained a network with a set of style images instead of a style image. This approach aimed to combine many style images created by one artist to synthesize a stylized image with the overall style of this artist. The dual style generative adversarial network (DualStyleGAN) [13] is proposed to characterize the content and style of a portrait by retaining an intrinsic style path to control the style of the original domain and an extrinsic path to model the style of the target extended domain. Peking Opera face makeup (POFMakeup) [14] also is a portrait style transfer method that can transfer the style of a portrait with a Peking Opera face to a target portrait. [PERSON] et al. [15] combined a universal style transfer method with image fusion and color enhancement methods to solve the problems of the color scheme, the strength of style strokes, and the adjustment of image contrast.  
To simultaneously handle multiple styles, [16] proposed a flexible conditional instance normalization approach embedded in style transfer networks to learn multiple styles, and [17] achieved multistyle generation in a generative network architecture with a learnable inspiration layer. [PERSON] et al. [18] adopted a mechanism and instance segmentation to achieve a regional multistyle style transfer model, which can solve the problem of unnatural connections between regions. [PERSON] et al. [19] combined various existing style transfer frameworks to propose a novel framework that can generate intriguing artistic stylization results by performing geometric deformation and using different styles from multiple artists.  
In AdaIN [5], adaptive instance normalization is implemented to train a network with various styles, providing the ability to transfer arbitrary styles after the training process. In WCT [6], the whitening and coloring transforms are adopted to synthesize arbitrary styles with a pretrained VGG network and a series of pretrained image restructuring decoders. Based on WCT, [PERSON] et al. [20] achieved the diversity of style transfer by adopting a deep feature perturbation (DFP) operation while preserving the quality of stylization results, and [PERSON] et al. [21] synthesized ultraresolution stylized images and reduced the convolutional filters by using a knowledge-distillation method. A style-attentional network (SANet) [22] is also an arbitrary style transfer method that can be used to efficiently generate stylized images by injecting local style patterns into content features on the basis of using the style attention mechanism.