Publication

Abstract : This research paper presents a thorough analysis of Image Super-Resolution (ISR) methods, covering their historical evolution, challenges, recent advances, and significance across domains. It explores both traditional interpolation-based and data-driven machine learning approaches, examining factors like image priors, network architectures, and the importance of domain-specific datasets and data augmentation. The paper also delves into ensemble models and addresses ongoing challenges in the field. It concludes with a comprehensive comparison of ISR methods, considering strengths, weaknesses, and applicability, offering a valuable resource for researchers and practitioners in the computer vision community and paving the way for future developments in image super-resolution.