基于深度学习的可见水印去除研究综述

IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Peixian Su, Yong Zhang
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引用次数: 0

摘要

随着深度学习技术的进步,深度学习方法越来越多地应用于图像恢复,特别是去除图像中可见水印的领域。这些方法发挥了重要作用,取得了显著成效。然而,对不同深度学习方法在图像水印去除领域的应用进行综述的文献非常少。本文从不同的角度对图像水印去除方法进行了比较研究。首先,我们来看看图像恢复技术的发展。其次,我们介绍了用于图像应用的深度学习网络的流行架构。然后,我们从监督和无监督的角度分析了基于深度学习的水印去除方法,并通过整合不同的网络架构和方法框架来分析各种深度学习方法的动机和原理。第三,我们从定量和定性两方面比较了这些常用的水印去除方法在公共水印数据集上的性能。最后,重点介绍了当前水印方法面临的挑战和潜在的研究方向。我们回顾和总结了基于深度学习的可见水印去除方法,旨在帮助评估现有的去除技术,并推动图像水印领域的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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来源期刊
Computational Intelligence
Computational Intelligence 工程技术-计算机:人工智能
CiteScore
6.90
自引率
3.60%
发文量
65
审稿时长
>12 weeks
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.
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