Multi-Stage Generative Upscaler: Reconstructing Football Broadcast Images via Diffusion Models
This addresses the need for detailed visuals in sports broadcasting for analysis and audience engagement, representing an incremental improvement with domain-specific adaptation.
The study tackled the problem of reconstructing low-resolution football broadcast images by introducing a multi-stage generative upscaling framework using Diffusion Models, which transformed 64x64 pixel inputs into 1024x1024 outputs and surpassed traditional methods in restoring textures and domain-specific details.
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.