Artistic Style Transfer using Deep Learning and Style Fusion- a Review

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In recent years, after the study ‘A Neural Algorithm of Artistic Style’ published by Gatys et al. in 2016b, research on style transfer boomed drastically. Style transfer is the process of copying an art style from a ‘style image’ to the contents of the ‘content image’ and producing a ‘draft image’ that is on par with respect to quality expectations. This paper explores different techniques of achieving style transformations namely Style Fusion and Convolutional Neural Networks (CNNs). Although CNNs are the state-of-the-art architecture to tackle cognitive visual tasks, and that they clearly perform much better than most conventional algorithms, the image processing-based style fusion method comes close to the CNN in terms of image output quality and supersedes in terms of time and computation and resources complexity. The procedure of both of these methods has been discussed in detail in this paper and it was concluded that CNNs have a lot more room for improvement that can be facilitated by the availability of better and larger datasets.