{"title":"基于深度学习的可见水印去除研究综述","authors":"Peixian Su, Yong Zhang","doi":"10.1111/coin.70072","DOIUrl":null,"url":null,"abstract":"<div>\n \n <p>With the advancement of deep learning technology, deep learning methods are increasingly applied to image restoration, especially in the field of visible watermark removal from images. These methods play an important role and have achieved remarkable success. However, there is a scarcity of literature summarizing the application of different deep learning methods in the field of image watermark removal. In this paper, we present a comparative study of image watermark removal methods from different perspectives. First, we take a look at the development of image restoration techniques. Second, we present the popular architectures of deep learning networks for image applications. Then, we analyze deep learning-based watermark removal methods from both supervised and unsupervised perspectives and provide insights into the motivation and principle of various deep learning methods, which will be analyzed by integrating different network architectures and methodological frameworks. Thirdly, we compare the performance of these popular watermark removal methods on public watermarked datasets in terms of quantitative and qualitative analysis. Finally, we highlight the challenges and potential research directions of current watermarking methods. We review and summarize deep learning-based methods for visible watermark removal, aiming to help evaluate existing removal techniques and advance the field of image watermarking.</p>\n </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 3","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep Learning for Visible Watermark Removal: A Survey\",\"authors\":\"Peixian Su, Yong Zhang\",\"doi\":\"10.1111/coin.70072\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n <p>With the advancement of deep learning technology, deep learning methods are increasingly applied to image restoration, especially in the field of visible watermark removal from images. These methods play an important role and have achieved remarkable success. However, there is a scarcity of literature summarizing the application of different deep learning methods in the field of image watermark removal. In this paper, we present a comparative study of image watermark removal methods from different perspectives. First, we take a look at the development of image restoration techniques. Second, we present the popular architectures of deep learning networks for image applications. Then, we analyze deep learning-based watermark removal methods from both supervised and unsupervised perspectives and provide insights into the motivation and principle of various deep learning methods, which will be analyzed by integrating different network architectures and methodological frameworks. Thirdly, we compare the performance of these popular watermark removal methods on public watermarked datasets in terms of quantitative and qualitative analysis. Finally, we highlight the challenges and potential research directions of current watermarking methods. We review and summarize deep learning-based methods for visible watermark removal, aiming to help evaluate existing removal techniques and advance the field of image watermarking.</p>\\n </div>\",\"PeriodicalId\":55228,\"journal\":{\"name\":\"Computational Intelligence\",\"volume\":\"41 3\",\"pages\":\"\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2025-06-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computational Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/coin.70072\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational Intelligence","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/coin.70072","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Deep Learning for Visible Watermark Removal: A Survey
With the advancement of deep learning technology, deep learning methods are increasingly applied to image restoration, especially in the field of visible watermark removal from images. These methods play an important role and have achieved remarkable success. However, there is a scarcity of literature summarizing the application of different deep learning methods in the field of image watermark removal. In this paper, we present a comparative study of image watermark removal methods from different perspectives. First, we take a look at the development of image restoration techniques. Second, we present the popular architectures of deep learning networks for image applications. Then, we analyze deep learning-based watermark removal methods from both supervised and unsupervised perspectives and provide insights into the motivation and principle of various deep learning methods, which will be analyzed by integrating different network architectures and methodological frameworks. Thirdly, we compare the performance of these popular watermark removal methods on public watermarked datasets in terms of quantitative and qualitative analysis. Finally, we highlight the challenges and potential research directions of current watermarking methods. We review and summarize deep learning-based methods for visible watermark removal, aiming to help evaluate existing removal techniques and advance the field of image watermarking.
期刊介绍:
This leading international journal promotes and stimulates research in the field of artificial intelligence (AI). Covering a wide range of issues - from the tools and languages of AI to its philosophical implications - Computational Intelligence provides a vigorous forum for the publication of both experimental and theoretical research, as well as surveys and impact studies. The journal is designed to meet the needs of a wide range of AI workers in academic and industrial research.