Developments in Image Processing using Deep learning and Reinforcement learning

The continuously surging volume of data, estimated to surpass 180 zettabytes by 2025, presents substantial challenges for both organizations and society as a whole. This influx of data not only brings about increased size but also introduces heightened complexity, presenting new theoretical and computational obstacles. Over the past two decades, the field of data science has experienced a notable surge in popularity due to its versatile applications in managing complex data, ensuring high accuracy, providing customizable flexibility, and demonstrating excellent adaptability. However, the analysis of image datasets introduces additional challenges, particularly when the goal is to enhance image quality, which in turn increases the volume of data for processing. Despite the continued dominance of traditional machine learning (ML) techniques in various research fields and industries, there is a growing interest among the scientific community in exploring innovative artificial intelligence (AI) approaches. The resurgence of neural networks, in particular, has led to significant breakthroughs, especially in the domains of image understanding and processing. This research conducts an extensive examination of the latest progress in designing and optimizing artificial intelligence (AI) solutions specifically tailored to tackle challenges in image processing. Despite the significant accomplishments, the field continues to grapple with various difficulties. The present study thoroughly explores essential and recent improvements, applications, and advancements within the sphere of image processing, offering insights into a domain characterized by continual and swift evolution. Additionally, the paper delineates prospective avenues for future research in this dynamic field.