Recent Advances in Image Super-Resolution: Exploring Diffusion Models, Wavelet-Based Approaches, and Federated Learning Techniques for High-Fidelity Image Enhancement

Abstract

Image super-resolution (SR) has become an essential field in computer vision, offering a means to enhance the resolution of low-quality images for applications in medical imaging, surveillance, remote sensing, and beyond. Traditional methods, based on interpolation techniques, have evolved significantly with the rise of deep learning, yielding more accurate and visually appealing results. However, as demands for higher quality and more efficient solutions grow, the field has witnessed a new wave of approaches, particularly involving diffusion models, wavelet-based techniques, and federated learning frameworks. Diffusion models, initially introduced for tasks in generative modeling, have shown remarkable potential in super-resolution by focusing on progressive denoising to restore high-frequency details. Hybrid methods, combining diffusion with wavelet transforms, present even greater promise by operating in both the spatial and frequency domains, enhancing the ability to capture fine textures in images. Wavelet-based approaches themselves, such as differential wavelet amplifiers, are proving invaluable in addressing the limitations of traditional CNN models, particularly in handling multi-scale image features. In parallel, federated learning introduces a distributed approach to training super-resolution models on decentralized data, ensuring privacy while optimizing computational efficiency. This paper provides a detailed overview of these cutting-edge techniques, highlighting their unique contributions and practical implications. We also explore the impact of dataset pruning methods in streamlining model training for large-scale applications. By bringing together these novel methods, this review offers insights into the future of super-resolution, where the integration of classical signal processing with modern machine learning will continue to drive the field forward.