The reconstruction of low-resolution football broadcast images presents a significant challenge in sports broadcasting, where detailed visuals are essential for analysis and audience engagement. This study introduces a multi-stage generative upscaling framework leveraging Diffusion Models to enhance degraded images, transforming inputs as small as 64×64 pixels into high-fidelity 1024×1024 outputs. By integrating an image-to-image pipeline, ControlNet conditioning, and LoRA fine-tuning, our approach surpasses traditional upscaling methods in restoring intricate textures and domain-specific elements such as player details and jersey logos. The custom LoRA is trained on a custom football dataset, ensuring adaptability to sports broadcast needs. Experimental results demonstrate substantial improvements over conventional models, with ControlNet refining fine details and LoRA enhancing task-specific elements. These findings highlight the potential of diffusion-based image reconstruction in sports media, paving the way for future applications in automated video enhancement and real-time sports analytics.
@article{arxiv.2503.11181,
title = {Multi-Stage Generative Upscaler: Reconstructing Football Broadcast Images via Diffusion Models},
author = {Luca Martini and Daniele Zolezzi and Saverio Iacono and Gianni Viardo Vercelli},
journal= {arXiv preprint arXiv:2503.11181},
year = {2026}
}