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It summarizes major AI/ML algorithms, the development of neural network models, and the principles, strengths, and limitations of different approaches. It then surveys application cases across scientific research fields and provides an outlook to support further research and cross-domain use.","TYPE Original Research PUBLISHED 14 October 2024  \nDOI 10.3389/fmats.2024.1431179  \nOPEN ACCESS  \nEDITED BY  \nFrancesca Borghi,  \nUniversity of Milan, Italy  \nREVIEWED BY  \nGianluca Milano,  \nNational Institute of Metrological Research (INRiM), Italy  \nFabio Michieletti,  \nPolytechnic University of Turin, Italy, in collaboration with reviewer GM Massimiliano Galluzzi,  \nChinese Academy of Sciences (CAS), China  \n*CORRESPONDENCE  \nXiangbing Gong,  \n [xbgong@csust.edu.cn](xbgong@csust.edu.cn)  \nRECEIVED 11 May 2024  \nACCEPTED 23 September 2024  \nPUBLISHED 14 October 2024  \nCITATION  \nXu P, Wang J, Jiang Y and Gong X (2024) Applications of artificial intelligence and machine learning in image processing.  \nFront. Mater. 11:1431179.  \ndoi: 10.3389/fmats.2024.1431179  \nCOPYRIGHT  \n© 2024 Xu, Wang, Jiang and Gong. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nApplications of artificial intelligence and machine learning in image processing  \nPingyuan Xu 1, Jinyuan Wang 2, Yu Jiang 2 and Xiangbing Gong 2* 1Zhejiang Infrastructure Construction Group Co., Ltd., Hangzhou, China, 2School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha, China  \nIntroduction: With the rapid development of artificial intelligence and machine learning technology, image processing technology based on artificial intelligence and machine learning has been applied in various fields, which effectively solves the multi-classification problem of similar targets in traditional image processing technology.  \nMethods: This paper summarizes the various algorithms of artificial intelligence and machine learning in image processing, the development process of neural network model, the principle of model and the advantages and disadvantages of different algorithms, and introduces the specific application of image processing technology based on these algorithms in different scientific research fields.  \nResults And Discussion: The application of artificial intelligence and machine learning in image processing is summarized and prospected, in order to provide some reference for researchers who used artificial intelligence and machine learning for image processing in different fields.  \nKEYWORDS  \nartificial intelligence and machine learning, artificial intelligence algorithm, neural network model, image processing and application, deep learning  \n1 Introduction  \nWith the development of information technology, great progress has been made in image feature analysis, image registration, image fusion, image classification, image recognition, content-based image retrieval, and image digital watermarking. These image processing techniques reflect human intelligent activity and mimic, extend and extend human intelligence on computers. They have intelligent processing capabilities. Due to the remarkable achievements of artificial intelligence research and application at home and abroad, the application effect of artificial intelligence technology in image processing will be huge.  \nThe idea of artificial intelligence and machine learning first emerged in the 1940sand 1950s, when people proposed the idea of artificial intelligence, machine learning also emerged. Many related scholars hoped to make computers intelligent and capable of continuous self-learning like humans. Turing (1950) created the Turing test to determine whether a computer is intelligent. But at that time, it was only in the initial stage. The level of artificial intelligence and machine learning was very low, and it was very difficult to realize intelligence. 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