International Journal of Science and Technology (IJST)

A Survey on Artificial Intelligence Applications in Image Processing Across Diverse Fields

Artificial Intelligence (AI) has emerged as a powerful tool in image processing by enabling automated analysis, feature extraction and intelligent decision-making from visual data. With advancements in computational power and data availability, AI-based image processing techniques are increasingly applied across multiple domains such as healthcare, agriculture, astronomy, finance, security and industrial automation. This paper presents a comprehensive survey of recent research works that integrate AI methods with image processing to address domain-specific challenges and improve system performance. Key techniques including Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), Support Vector Machines (SVM), Optical Character Recognition (OCR) and hybrid models are systematically reviewed and compared. Their applications in tasks such as medical image segmentation, weed detection, satellite image enhancement, astronomical image preprocessing and document automation are discussed. The survey highlights significant improvements in accuracy, efficiency and automation achieved through AI-driven approaches when compared to traditional image processing techniques. In addition, major challenges such as limited annotated datasets, high computational requirements, generalization issues and ethical concerns related to privacy are identified. Finally, the paper outlines future research directions including explainable AI, federated learning and lightweight models for real-time and edge-based deployment. This study aims to serve as a useful reference for researchers and practitioners seeking to apply AI techniques in image processing across diverse real-world applications.