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Edge Enhanced Image Style Transfer via Transformers | | Chiyu Zhang
; Jun Yang
; Zaiyan Dai
; Peng Cao
; | | Date: |
2 Jan 2023 | | Abstract: | In recent years, arbitrary image style transfer has attracted more and more
attention. Given a pair of content and style images, a stylized one is hoped
that retains the content from the former while catching style patterns from the
latter. However, it is difficult to simultaneously keep well the trade-off
between the content details and the style features. To stylize the image with
sufficient style patterns, the content details may be damaged and sometimes the
objects of images can not be distinguished clearly. For this reason, we present
a new transformer-based method named STT for image style transfer and an edge
loss which can enhance the content details apparently to avoid generating
blurred results for excessive rendering on style features. Qualitative and
quantitative experiments demonstrate that STT achieves comparable performance
to state-of-the-art image style transfer methods while alleviating the content
leak problem. | | Source: | arXiv, 2301.00592 | | Services: | Forum | Review | PDF | Favorites | |
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