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arXiv · 2503.11181

Multi-Stage Generative Upscaler: Reconstructing Football Broadcast Images via Diffusion Models

Luca Martini·Daniele Zolezzi·Saverio Iacono·Gianni Viardo Vercelli
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Abstract

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 $64 \times 64$ pixels into high-fidelity $1024 \times 1024$ 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.

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Luca Martini, Daniele Zolezzi, Saverio Iacono, Gianni Viardo Vercelli. 2025-03-14. Multi-Stage Generative Upscaler: Reconstructing Football Broadcast Images via Diffusion Models. https://doi.org/10.1038/s41598-025-31543-8

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