State-of-the-art image style transfer methods have achieved impressive results by using neural networks. However, neural style transfer (NST) methods either ignore the local details of the style image by using the global statistics for style modeling or cannot fully use shallow features of neural networks, leading to the synthesized image having fewer details. In this study, we proposed a new patch-based style transfer method that directly operates in the image pixel domain without using any neural networks, achieving fascinating style transfer results with rich image details. The proposed method was derived from classic texture synthesis methods. Most previous methods rely on nearest neighbor search (NNS) for patch matching. However, this greedy strategy cannot guarantee the similarity of patch distributions between the synthesized image and the style image, which limits the expressiveness of textures. We solved this problem by proposing an optimal patch matching algorithm formed on the Optimal Transport (OT) theory, which theoretically guarantees the similarity of the patch distributions and gives a flexible style modeling method. Various qualitative and quantitative experiments demonstrated that the proposed method achieves better synthesized results than state-of-the-art style transfer methods, including NST and classic methods based on texture synthesis.

Abstract Arbitrary style transfer aims to stylize the content image with the style image. The key problem of style transfer is how to balance the global content structure and the local style patterns. A promising method to solve this problem is the attentional style transfer method, where a learnable embedding of image features enables style patterns to be flexibly recombined with the content image, so local style patterns will be well preserved in the stylized image. However, current attentional style transfer methods cannot well preserve the global content structure. To solve this problem, a novel attentional style transfer network is proposed, that relies on Optimal Transport (OT) for computing the attention map. The proposed OT‐based attention ensures the similarity between global distributions of the synthesized image and its corresponding style image. For the optimal transport computation, a regularized formulation is used, which not only allows an unbalanced optimal transport to address the deviational distributions but also improves the robustness of stylized results. The proposed method finds a well balance between the global content structure and local style patterns. Various experiments are conducted to demonstrate the superiority of the proposed method over state‐of‐the‐art methods.

Example-based texture synthesis aims to generate a new texture from an exemplar texture and has long been drawing attention in the fields of computer graphics, computer vision, and image processing. Nevertheless, synthesizing structured textures remains a challenging task. Most previous methods rely on additional guidance channels, which encode the structured features of textures. However, estimating the guidance channel is very difficult, and often fails when a texture has unpronounced features. In this paper, we propose a novel texture synthesis method, based on non-local operators, which captures the long-range structure of a texture without the additional guidance channel. The synthesized texture is generated by minimizing non-local texture energy through an expectation-maximization like optimization algorithm. A statistical constraint based on the Wasserstein distance is also proposed to ensure that the synthesized texture preserves the global statistics of the exemplar texture. Extensive experiments show that the proposed method can stably handle textures with different scale structures.

Woodcut is a painting, in which knife and woodblock are used instead of pen and paper. Yunnan out-of-print woodcut is an extension of the traditional woodcut. In this paper, we introduce an automatic image-based approach with three steps. First, image is sliced out by color clustering. Then, scores collected from real woodcuts are placed automatically in segmenting regions. Finally, a virtual printing method combining all regions with their colors is proposed to synthesize multicolor woodcut. This method is simple and efficient, and the simulated result looks good.