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Open AccessArticle

Fast Continuous Structural Similarity Patch Based Arbitrary Style Transfer

Shanghai Film Academy, Shanghai University, Shanghai 200072, China
School of Literature and Media, Taishan University, Tai’an 271021, China
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(16), 3304;
Received: 17 July 2019 / Revised: 30 July 2019 / Accepted: 8 August 2019 / Published: 12 August 2019
(This article belongs to the Special Issue Advanced Intelligent Imaging Technology)
PDF [7725 KB, uploaded 12 August 2019]


Style transfer is using a pair of content and style images to synthesize a stylized image which has both the structure of the content image and the style of style image. Existing optimization-based methods are limited in their performance. Some works using a feed-forward network allow arbitrary style transfer but cannot reflect the style. In this paper, we present a fast continuous structural similarity patch based arbitrary style transfer. Firstly, we introduce the structural similarity index (SSIM) to compute the similarity between all of the content and style patches for obtaining their similarity. Then a local style patch choosing procedure is applied to maximize the utilization of all style patches and make the swapped style patch continuous matching with respect to the spatial location of style at the same time. Finally, we apply an efficient trained feed-forward inverse network to obtain the final stylized image. We use more than 80,000 natural images and 120,000 style images to train that feed-forward inverse network. The results show that our method is able to transfer arbitrary style with consistency, and the result comparison stage is made to show the effectiveness and high-quality of our stylized images. View Full-Text
Keywords: style transfer; similarity structure patch; continuity of local style patch; feed-forward network style transfer; similarity structure patch; continuity of local style patch; feed-forward network

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Wu, B.; Ding, Y.; Dong, Q. Fast Continuous Structural Similarity Patch Based Arbitrary Style Transfer. Appl. Sci. 2019, 9, 3304.

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