Reproduce “Geodesic Diffusion Models for Efficient Medical Image Enhancement” on OpenResearch
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About this paper
Teng Zhang, Hongxu Jiang, Kuang Gong, Wei Shao
Researchers at the University of Florida developed Geodesic Diffusion Models (GDM), a framework that derives noise schedules based on geodesic paths in probability space to improve the efficiency and performance of diffusion models for medical image enhancement. The approach demonstrated state-of-the-art results in CT denoising and MRI super-resolution, reducing training time by up to 33 times and inference time by up to 171 times compared to conventional methods.
Key insights
- The high computational cost of diffusion models can be addressed by moving from empirical noise schedules to mathematically derived geodesic paths in probability space.
- Leveraging information geometry and the Fisher–Rao metric allows for the derivation of optimal diffusion trajectories that inherently minimize redundant intermediate states.
- By ensuring the diffusion process follows constant-speed geodesic paths, both training convergence and sampling efficiency can be significantly improved without compromising generative quality.
Summary by alphaXiv.
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