# Coarse-to-Fine Structure-Aware Artistic Style Transfer
## 1 Introduction  
Artistic style transfer is an attractive image-processing technique that is used to generate a new image that preserves the structure of a content image but carries the pattern of a style image. Recently, the seminal image-optimization method proposed by [PERSON] et al. [1] was used to achieve style transfer by adopting the correlation of features extracted from a pretrained deep neural network and the iterative optimization process. Like the method presented by [PERSON] et al. [1], style transfer by relaxed optimal transport and self-similarity (STROTSS) [2] is also an image-optimization style transfer method; this method has achieved superior stylization results by adopting the relaxed earth mover's distance (rEMD) loss in a multiscale optimization process. However, the expensive computational cost of these image-optimization methods restricts their use in practice applications in industry. To speed up the optimization procedure, [PERSON] et al. [3] and [PERSON] et al. [4] proposed model-optimization style transfer methods. They train a feed-forward neural network that can be used to synthesize images with a single given style image in real time. Both adaptive instance normalization (AdaIN) [5] and whitening and coloring transforms (WCTs) [6] are model-optimization methods but are also arbitrary style transfer methods, in which style patterns of arbitrary style images are transferred by adopting some feature transforms. After reviewing these methods, we have found that although local style texture and content structures can generally be combined, some key structures of the style image are not accurately learned. For example, the color blocks and brushstrokes that constitute the main objects in style images are not transferred very well. Meanwhile, in some cases, these methods produce distorted objects and incongruous artistic effects in stylized images. Therefore, our main task is to transfer the local structure of the style image to the contentimage and adopt a coarse-to-fine strategy to enhance the artistic details of the stylization results. We propose a novel artistic style transfer network for fusing an essential style structure to a content structure and synthesizing a structure-aware stylized image. In our model, a coarse network is designed to obtain reconstructed coarse stylized features in the first stage. Because the coarse network works only at a low resolution, the coarse stylized features can discard the trivial structure details of the content image and combine the global content structure with the style patterns. Then, the task of a fine network is to adopt these reconstructed coarse stylized features obtained at a low resolution and the original content image with a high resolution to synthesize the final high-resolution stylized image in the second stage. By adopting some SSF modules to fuse the coarse stylized features into the fine network, the final high-resolution stylized images can selectively integrate structural information at different scales. Our main contributions are as follows:  
1. We introduce a novel style transfer model that can be used to synthesize appealing structure-aware stylization results. This model consists of a coarse network and a fine network. The former roughly transfers style patterns that include holistic structural information and color distribution information, and then the latter enhances the details of the style patterns by fusing multiscale features. 2. We propose a SSF module for fusing the reconstructed features to the content features in a fine network. This module can help the fine network select essential structural information for feature fusion on the basis of the channel attention mechanism. As a result, the color distribution of the style images can be accurately transferred. 3. It is demonstrated through experiments that our method can be used to synthesize high-quality stylizations, where the main structures of the content image are preserved and the local structures of the style image are transferred.