A Comprehensive Review Of Image Processing Techniques: Classical Methods, Deep Learning, Applications, And Recent Advances

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Image processing has evolved from conventional pixel-level operations to sophisticated artificial intelligence (AI)-driven approaches capable of automated visual understanding, interpretation, and decision support. This review provides a comprehensive overview of image processing techniques, their methodological evolution, major applications, and recent advances. The review examines fundamental image representation and quality assessment, preprocessing, image enhancement, restoration, segmentation, feature extraction, and image compression, followed by modern deep learning approaches based on convolutional neural networks (CNNs), transfer learning, Vision Transformers, generative AI, and foundation models. A structured narrative literature review and thematic comparative analysis were used to organize the literature according to processing technique, learning paradigm, computational requirements, interpretability, and deployment potential. Classical techniques remain valuable because of their computational efficiency, simplicity, and suitability for controlled image-processing tasks, whereas deep learning approaches enable automatic hierarchical feature learning and improved performance for complex visual analysis. Recent developments in Transformers, explainable AI (XAI), generative models, and vision foundation models have further expanded the capabilities of image processing by supporting contextual understanding, visual explanation, image synthesis, and generalization across tasks and domains. Applications across medical imaging, agriculture, remote sensing, autonomous transportation, industrial inspection, security and surveillance, multimedia, environmental monitoring, and biometrics demonstrate the broad relevance of these technologies. The review also highlights persistent challenges involving data requirements, computational cost, model interpretability, robustness, privacy, domain generalization, and real-time deployment. Future research is expected to increasingly integrate efficient AI architectures, explainability, edge computing, multimodal learning, and foundation models to develop scalable and trustworthy intelligent image-processing systems.