Image Inpainting by Machine Learning Algorithms

IF 0.7 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Qing Bu, Wei Wan, Ivan Leonov
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引用次数: 0

Abstract

Image inpainting is the process of filling in missing or damaged areas of images. In recent years, this area has received significant development, mainly owing to machine learning methods. Generative adversarial networks are a powerful tool for creating synthetic images. They are trained to create images similar to the original dataset. The use of such neural networks is not limited to creating realistic images. In areas where privacy is important, such as healthcare or finance, they help generate synthetic data that preserves the overall structure and statistical characteristics, but does not contain the sensitive information of individuals. However, direct use of this architecture will result in the generation of a completely new image. In the case where it is possible to indicate the location of confidential information on an image, it is advisable to use image inpainting in order to replace only the secret information with synthetic information. This paper discusses key approaches to solving this problem, as well as corresponding neural network architectures. Questions are also raised about the use of these algorithms to protect confidential image information, as well as the possibility of using these models when developing new applications.

Abstract Image

用机器学习算法绘制图像
摘要 图像内绘是对图像缺失或损坏区域进行填充的过程。近年来,这一领域得到了长足的发展,这主要归功于机器学习方法。生成对抗网络是创建合成图像的强大工具。经过训练,它们可以创建与原始数据集相似的图像。这类神经网络的使用不仅限于创建逼真的图像。在医疗保健或金融等重视隐私的领域,它们有助于生成保留整体结构和统计特征,但不包含个人敏感信息的合成数据。不过,直接使用这种架构会生成全新的图像。如果有可能在图像上标出机密信息的位置,最好使用图像涂抹技术,只用合成信息替换机密信息。本文讨论了解决这一问题的主要方法以及相应的神经网络架构。本文还提出了使用这些算法保护机密图像信息的问题,以及在开发新应用时使用这些模型的可能性。
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来源期刊
PATTERN RECOGNITION AND IMAGE ANALYSIS
PATTERN RECOGNITION AND IMAGE ANALYSIS Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
1.80
自引率
20.00%
发文量
80
期刊介绍: The purpose of the journal is to publish high-quality peer-reviewed scientific and technical materials that present the results of fundamental and applied scientific research in the field of image processing, recognition, analysis and understanding, pattern recognition, artificial intelligence, and related fields of theoretical and applied computer science and applied mathematics. The policy of the journal provides for the rapid publication of original scientific articles, analytical reviews, articles of the world''s leading scientists and specialists on the subject of the journal solicited by the editorial board, special thematic issues, proceedings of the world''s leading scientific conferences and seminars, as well as short reports containing new results of fundamental and applied research in the field of mathematical theory and methodology of image analysis, mathematical theory and methodology of image recognition, and mathematical foundations and methodology of artificial intelligence. The journal also publishes articles on the use of the apparatus and methods of the mathematical theory of image analysis and the mathematical theory of image recognition for the development of new information technologies and their supporting software and algorithmic complexes and systems for solving complex and particularly important applied problems. The main scientific areas are the mathematical theory of image analysis and the mathematical theory of pattern recognition. The journal also embraces the problems of analyzing and evaluating poorly formalized, poorly structured, incomplete, contradictory and noisy information, including artificial intelligence, bioinformatics, medical informatics, data mining, big data analysis, machine vision, data representation and modeling, data and knowledge extraction from images, machine learning, forecasting, machine graphics, databases, knowledge bases, medical and technical diagnostics, neural networks, specialized software, specialized computational architectures for information analysis and evaluation, linguistic, psychological, psychophysical, and physiological aspects of image analysis and pattern recognition, applied problems, and related problems. Articles can be submitted either in English or Russian. The English language is preferable. Pattern Recognition and Image Analysis is a hybrid journal that publishes mostly subscription articles that are free of charge for the authors, but also accepts Open Access articles with article processing charges. The journal is one of the top 10 global periodicals on image analysis and pattern recognition and is the only publication on this topic in the Russian Federation, Central and Eastern Europe.
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