Version 1
: Received: 2 July 2024 / Approved: 2 July 2024 / Online: 3 July 2024 (10:46:04 CEST)
How to cite:
Pan, L.; Wan, W.; Sun, W.; Zeng, Q.; Xu, J. Combine Deep Learning and Artificial Intelligence to Optimize the Application Path of Digital Image Processing Technology. Preprints2024, 2024070270. https://doi.org/10.20944/preprints202407.0270.v1
Pan, L.; Wan, W.; Sun, W.; Zeng, Q.; Xu, J. Combine Deep Learning and Artificial Intelligence to Optimize the Application Path of Digital Image Processing Technology. Preprints 2024, 2024070270. https://doi.org/10.20944/preprints202407.0270.v1
Pan, L.; Wan, W.; Sun, W.; Zeng, Q.; Xu, J. Combine Deep Learning and Artificial Intelligence to Optimize the Application Path of Digital Image Processing Technology. Preprints2024, 2024070270. https://doi.org/10.20944/preprints202407.0270.v1
APA Style
Pan, L., Wan, W., Sun, W., Zeng, Q., & Xu, J. (2024). Combine Deep Learning and Artificial Intelligence to Optimize the Application Path of Digital Image Processing Technology. Preprints. https://doi.org/10.20944/preprints202407.0270.v1
Chicago/Turabian Style
Pan, L., Qiang Zeng and Jingyu Xu. 2024 "Combine Deep Learning and Artificial Intelligence to Optimize the Application Path of Digital Image Processing Technology" Preprints. https://doi.org/10.20944/preprints202407.0270.v1
Abstract
Artificial intelligence provides a new research concept for digital image processing. However, at present, artificial intelligence is rarely introduced into the teaching of digital image processing in colleges and universities, and there are problems such as obsolete teaching content, single teaching methods, and simple course experiments, which affect the teaching effect and are not conducive to the cultivation of comprehensive and innovative talents. Digital image processing technology brings more possibilities to communication engineering and makes communication more convenient. For example, video calls and photo transmission make people's communication methods in daily life more and more diversified. Time and space limitations allow people to meet online, creating more communication possibilities. However, there are still many problems and methods worthy of in-depth exploration. Therefore, this paper has a comprehensive understanding and mastery of the traditional and deep learning methods of digital image processing to improve the relevant project practice and scientific research exploration ability and refer for similar research conclusions.
Keywords
Artificial intelligence; Digital image processing; Communication engineering; Deep learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
