IMAGE PROCESSING THEN & NOW: A SYSTEMATIC LITERATURE REVIEW

Abstract

This research paper presents a systematic literature review of advancements in image processing from 2011 to 2020, focusing on key technological innovations, emerging techniques, and future directions. The study employs a comprehensive search strategy across multiple databases, including IEEE Xplore, ACM Digital Library, and Google Scholar, to capture significant research contributions in the field. The review highlights transformative advancements driven by deep learning techniques, such as Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs), which have significantly enhanced image classification, generation, and enhancement. Improvements in image segmentation methods, exemplified by U-Net and Mask R-CNN, have enabled precise analysis and localization in complex imaging tasks, particularly in medical contexts. Real-time image processing has been revolutionized by models like YOLO and Inception, facilitating efficient and accurate detection in dynamic environments. The study also explores the impact of generative models, such as Variational Autoencoders (VAEs), on image synthesis and enhancement, and the integration of multimodal data for improved image understanding. Notable advancements in medical imaging, including dermatological analysis and brain tumor segmentation, are also discussed. The findings provide a comprehensive overview of the state-of-the-art in image processing and underscore the need for ongoing innovation to address emerging challenges and expand application areas. The paper concludes with insights into future research directions and the potential for further advancements in image processing technologies.