You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Drawing a beautiful painting is a dream of many people since childhood. In this paper, we propose a novel scheme, Line Artist, to synthesize artistic style paintings with freehand sketch images, leveraging the power of deep learning and advanced algorithms. Our scheme includes three models. The Sketch Image Extraction (SIE) model is applied to generate the training data. It includes smoothing reality images and pencil sketch extraction. The Detailed Image Synthesis (DIS) model trains a conditional generative adversarial network to generate detailed real-world information. The Adaptively Weighted Artistic Style Transfer (AWAST) model is capable to combine multiple style images with a content with the VGG19 network and PageRank algorithm. The appealing artistic images are then generated by optimization iterations. Experiments are operated on the Kaggle Cats dataset and The Oxford Buildings Dataset. Our synthesis results are proved to be artistic, beautiful and robust.

Cited Abstract(s):
[1]: In this paper, the authors introduce TextRank, a graph-based ranking model for text processing, and show how this model can be successfully used in natural language applications.

[2]: recently demonstrated that deep networks can generate beautiful textures and stylized images from a single texture example. However, their methods requires a slow and memory-consuming optimization process. We propose here an alternative approach that moves the computational burden to a learning stage. Given a single example of a texture, our approach trains compact feed-forward convolutional networks to generate multiple samples of the same texture of arbitrary size and to transfer artistic style from a given image to any other image. The resulting networks are remarkably light-weight and can generate textures of quality comparable to Gatys et al., but hundreds of times faster. More generally, our approach highlights the power and flexibility of generative feed-forward models trained with complex and expressive loss functions.

[3]: 

[4]: The importance of a Web page is an inherently subjective matter, which depends on the readers interests, knowledge and attitudes. But there is still much that can be said objectively about the relative importance of Web pages. This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them. We compare PageRank to an idealized random Web surfer. We show how to efficiently compute PageRank for large numbers of pages. And, we show how to apply PageRank to search and to user navigation.

[5]: In fine art, especially painting, humans have mastered the skill to create unique visual experiences through composing a complex interplay between the content and style of an image. Thus far the algorithmic basis of this process is unknown and there exists no artificial system with similar capabilities. However, in other key areas of visual perception such as object and face recognition near-human performance was recently demonstrated by a class of biologically inspired vision models called Deep Neural Networks. Here we introduce an artificial system based on a Deep Neural Network that creates artistic images of high perceptual quality. The system uses neural representations to separate and recombine content and style of arbitrary images, providing a neural algorithm for the creation of artistic images. Moreover, in light of the striking similarities between performance-optimised artificial neural networks and biological vision, our work offers a path forward to an algorithmic understanding of how humans create and perceive artistic imagery.

[6]: Non-photorealistic rendering is an important research topic in computer graphics, where painterly or stroke-based rendering has received intensive attention from researchers in recent years. The goal of this paper is to design a fully automatic algorithm, which is able to turn a photograph into an oil-style painting. Different from the existing approaches that use real brush stroke images as templates, our brush strokes were created in random manner according to the characteristics of the local image region. For determining the direction of a brush stroke, we also proposed a new method based on template-matching to evaluate the major orientation of edge features within a local image window. Moreover, a novel method of deciding stroke locations was proposed, which is simple yet effective. All these features together significantly reduce the undesirable systematic impression, which appears to be a common artifact of painterly rendering.

[7]: Style transfer is a process of migrating a style from a given image to the content of another, synthesizing a new image, which is an artistic mixture of the two. Recent work on this problem adopting convolutional neural-networks (CNN) ignited a renewed interest in this field, due to the very impressive results obtained. There exists an alternative path toward handling the style transfer task, via the generalization of texture synthesis algorithms. This approach has been proposed over the years, but its results are typically less impressive compared with the CNN ones. In this paper, we propose a novel style transfer algorithm that extends the texture synthesis work of (2005), while aiming to get stylized images that are closer in quality to the CNN ones. We modify Kwatra’s algorithm in several key ways in order to achieve the desired transfer, with emphasis on a consistent way for keeping the content intact in selected regions, while producing hallucinated and rich style in others. The results obtained are visually pleasing and diverse, shown to be competitive with the recent CNN style transfer algorithms. The proposed algorithm is fast and flexible, being able to process any pair of content + style images.
Most researches about style transfer focus on the combination of content and single style. [6] , initial content is transformed into oil style by pixel-level analysis. [5] proposed a style transfer scheme based on CNN whose results are quite appealing. The model in [3] combines markov random fields with [5] . [2] , a faster feed-forward style image synthesis network is proposed. Texture synthesis is used to transfer image style in [7] . In this paper, multiple styles are combined together to synthesize appealing results with PageRank [4] in undirected graph [1] .