research-article

RAST: Restorable Arbitrary Style Transfer

Authors:

Anup BasuAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications and Applications, Volume 20, Issue 5

Article No.: 143, Pages 1 - 21

https://doi.org/10.1145/3638770

Published: 22 January 2024 Publication History

Abstract

The objective of arbitrary style transfer is to apply a given artistic or photo-realistic style to a target image. Although current methods have shown some success in transferring style, arbitrary style transfer still has several issues, including content leakage. Embedding an artistic style can result in unintended changes to the image content. This article proposes an iterative framework called Restorable Arbitrary Style Transfer (RAST) to effectively ensure content preservation and mitigate potential alterations to the content information. RAST can transmit both content and style information through multi-restorations and balance the content-style tradeoff in stylized images using the image restoration accuracy. To ensure RAST’s effectiveness, we introduce two novel loss functions: multi-restoration loss and style difference loss. We also propose a new quantitative evaluation method to assess content preservation and style embedding performance. Experimental results show that RAST outperforms state-of-the-art methods in generating stylized images that preserve content and embed style accurately.

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Cited By

Chen YLong YYang ZLong J(2025)Parameter Adaptive Contrastive Hashing for multimedia retrievalNeural Networks10.1016/j.neunet.2024.106923182(106923)Online publication date: Feb-2025
https://doi.org/10.1016/j.neunet.2024.106923
Hu ZGe BXia C(2025)Multiangle feature fusion network for style transferImage and Vision Computing10.1016/j.imavis.2024.105386154(105386)Online publication date: Feb-2025
https://doi.org/10.1016/j.imavis.2024.105386
Hu ZGe BXia CWu WZhou GWang B(2024)Flow style-aware network for arbitrary style transferComputers and Graphics10.1016/j.cag.2024.104098124:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.cag.2024.104098

Index Terms

RAST: Restorable Arbitrary Style Transfer
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 20, Issue 5

May 2024

650 pages

EISSN:1551-6865

DOI:10.1145/3613634

Editor:
Abdulmotaleb El Saddik
Mohamed Bin Zayed University of Artificial Intelligence, UAE and University of Ottawa, Canada

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Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 January 2024

Online AM: 30 December 2023

Accepted: 21 December 2023

Revised: 21 November 2023

Received: 05 June 2023

Published in TOMM Volume 20, Issue 5

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Cited By

Chen YLong YYang ZLong J(2025)Parameter Adaptive Contrastive Hashing for multimedia retrievalNeural Networks10.1016/j.neunet.2024.106923182(106923)Online publication date: Feb-2025
https://doi.org/10.1016/j.neunet.2024.106923
Hu ZGe BXia C(2025)Multiangle feature fusion network for style transferImage and Vision Computing10.1016/j.imavis.2024.105386154(105386)Online publication date: Feb-2025
https://doi.org/10.1016/j.imavis.2024.105386
Hu ZGe BXia CWu WZhou GWang B(2024)Flow style-aware network for arbitrary style transferComputers and Graphics10.1016/j.cag.2024.104098124:COnline publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1016/j.cag.2024.104098

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