Artificial intelligence-based methods for image processing
Artificial intelligence-based methods for image processing
Abstract
Abstract.
Artificial intelligence (AI) has revolutionized the field of image processing by introducing advanced techniques for image analysis, enhancement, segmentation, and recognition. This paper comprehensively reviews AI-based methods employed in image processing tasks. It covers various AI techniques, such as machine learning, deep learning, and their applications in medical imaging, computer vision, robotic system, and more.
References
A. K. Adithya, K. Ramesh, and N. Hemalatha, ―Machine Learning in Image Processing - a Survey,‖ Int. J. Latest Trends Eng. Technol., no. Special Issue SACAIM, pp. 425–428, 2016.
X. Zhang and W. Dahu, ―Application of artificial intelligence algorithms in image processing,‖ J. Vis. Commun. Image Represent., vol. 61, pp. 42–49, 2019.
L. Kumari, ―A Study of AI based Technique in Image Processing,‖ Int. J. Multidiscip. Res., vol. 5, no. 2, pp. 1–6, 2023.
R. Archana and P. S. E. Jeevaraj, Deep learning models for digital image processing: a review, vol. 57, no. 1. Springer Netherlands, 2024.
J. Valente, J. António, C. Mora, and S. Jardim, ―Developments in Image Processing using Deep learning and Reinforcement learning,‖ J. Imaging, vol. 9, no. 10, p. 207, 2023.
Z. Rudnicka, J. Szczepanski, and A. Pregowska, ―Artificial Intelligence-Based Algorithms in Medical Image Scan Segmentation and Intelligent Visual Content Generation—A Concise Overview,‖ Electron., vol. 13, no. 4, 2024.
Q. Qiao, ―Image Processing Technology Based on Machine Learning,‖ IEEE Consum. Electron. Mag., 2022.
A. Heena, N. Biradar, N. M. Maroof, S. Bhatia, R. Agarwal, and K. Prasad, ―Machine learning based biomedical image processing for echocardiographic images,‖ Multimed. Tools Appl., vol. 82, no. 25, pp. 39601–39616, 2023.
N. Rahmatov, A. Paul, F. Saeed, W. H. Hong, H. C. Seo, and J. Kim, ―Machine learning–based automated image processing for quality management in industrial Internet of Things,‖ Int. J. Distrib. Sens. Networks, vol. 15, no. 10, 2019.
X. V. Wang, J. S. Pinter, Z. Liu, and L. Wang, ―A machine learning-based image processing approach for robotic assembly system,‖ Procedia CIRP, vol. 104, no. March, pp. 906–911, 2021.
S. Masubuchi et al., ―Deep-learning-based image segmentation integrated with optical microscopy for automatically searching for two-dimensional materials,‖ npj 2D Mater. Appl., vol. 4, no. 1, pp. 4–6, 2020.
Q. Lv, S. Zhang, and Y. Wang, ―Deep Learning Model of Image Classification Using Machine Learning,‖ Adv. Multimed., vol. 2022, 2022.
T. Park, T. K. Kim, Y. D. Han, K. A. Kim, H. Kim, and H. S. Kim, ―Development of a deep learning based image processing tool for enhanced organoid analysis,‖ Sci. Rep., vol. 13, no. 1, pp. 1–11, 2023.
Z. Guo, X. Li, H. Huang, N. Guo, and Q. Li, ―Deep learning-based image segmentation on multimodal medical imaging,‖ IEEE Trans. Radiat. Plasma Med. Sci., vol. 3, no. 2, pp. 162–169, 2019.
S. Walia, K. Kumar, S. Agarwal, and H. Kim, ―Using XAI for Deep Learning-Based Image Manipulation Detection with Shapley Additive Explanation,‖ Symmetry (Basel)., vol. 14, no. 8, 2022.
