Advancements in Image Super-Resolution: Diffusion Models, Wavelets, and Federated Learning

Abstract

Image super-resolution (ISR) has seen tremendous advancements over the past few years, driven primarily by novel techniques in diffusion models, wavelet-based transformations, and federated learning approaches. This paper aims to provide a comprehensive overview of these advancements by exploring key methods such as the application of diffusion models, wavelet amplifications, and federated learning architectures in the context of ISR. We investigate the role of deep learning architectures, highlighting their capacity to enhance image quality by recovering high-frequency details from low-resolution images. Several approaches—such as the Differential Wavelet Amplifier (DWA), diffusion-wavelet hybrid methods, and area-masked diffusion—are discussed. Further, we examine the integration of federated learning in blind super-resolution, and we assess the impact of dataset pruning in optimizing ISR models. Collectively, these advancements pave the way for more efficient and robust ISR techniques applicable across diverse domains, including medical imaging, remote sensing, and video enhancement. This paper consolidates research findings from a variety of sources, offering insights into future directions for ISR technology. Through a detailed analysis of the most recent developments, this work highlights the evolving landscape of ISR methodologies and their applications.