Image Arbitrary Style Transfer via Artistic Aesthetic Enhancement

Current research has shown remarkable success in universal style transfer,which can transfer arbitrary visual styles to content images.However,in the evaluation dimension of arbitrary style transfer of images,it is not comprehensive to only consi-der the retention of semantic structure and the diversity of style patterns,and the artistic aesthetics should also be taken into account.Existing methods generally have the problem of artistic aesthetic unnaturalness,which is manifested in the disharmonious patterns and obvious artifacts in the stylized images which are easy to distinguish from the real paintings.To solve this problem,a novel artistic aesthetic enhancement image arbitrary style transfer(AAEST) approach is proposed.Specifically,first,a multi-scale artistic aesthetic enhancement module is designed to improve the problem of disharmonious patterns by extracting style image features at different scales.At the same time,an aesthetic-style attention module is designed,which uses the channel attention mechanism to adaptively match and enhance style features according to the global aesthetic channel distribution of the aesthetic features.Finally,a covariance transformation fusion module is proposed to transfer the second-order statistics of the enhanced style features to the corresponding content features,so as to achieve aesthetic-enhanced style transfer while preserving the content structure.The effectiveness of the proposed module and the added loss function are verified by qualitative comparison with the latest four style transfer methods and ablation experiments.In the comparison of five quantitative indicators,four achieve optimal scores.Experimental results show that the proposed method can generate more harmonious style transfer images.

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Publication Details

Journal
SHILAP Revista de lepidopterología
Published
2024-09-01
DOI
https://doi.org/10.11896/jsjkx.230800098
Primary Topic
Agriculture, Water, and Health
Type
article
Field-Weighted Citation Impact
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article

Image Arbitrary Style Transfer via Artistic Aesthetic Enhancement

SHILAP Revista de lepidopterología
Agriculture, Water, and Health
article

Image Arbitrary Style Transfer via Artistic Aesthetic Enhancement

article en

Abstract

Current research has shown remarkable success in universal style transfer,which can transfer arbitrary visual styles to content images.However,in the evaluation dimension of arbitrary style transfer of images,it is not comprehensive to only consi-der the retention of semantic structure and the diversity of style patterns,and the artistic aesthetics should also be taken into account.Existing methods generally have the problem of artistic aesthetic unnaturalness,which is manifested in the disharmonious patterns and obvious artifacts in the stylized images which are easy to distinguish from the real paintings.To solve this problem,a novel artistic aesthetic enhancement image arbitrary style transfer(AAEST) approach is proposed.Specifically,first,a multi-scale artistic aesthetic enhancement module is designed to improve the problem of disharmonious patterns by extracting style image features at different scales.At the same time,an aesthetic-style attention module is designed,which uses the channel attention mechanism to adaptively match and enhance style features according to the global aesthetic channel distribution of the aesthetic features.Finally,a covariance transformation fusion module is proposed to transfer the second-order statistics of the enhanced style features to the corresponding content features,so as to achieve aesthetic-enhanced style transfer while preserving the content structure.The effectiveness of the proposed module and the added loss function are verified by qualitative comparison with the latest four style transfer methods and ablation experiments.In the comparison of five quantitative indicators,four achieve optimal scores.Experimental results show that the proposed method can generate more harmonious style transfer images.

SHILAP Revista de lepidopterología
Yunnan University (CN)
Sustainable cities and communities
Openalex Percentile: Top 54%
Agriculture, Water, and Health
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