  The development of medical imaging techniques has made a significant
contribution to clinical decision-making. However, the existence of suboptimal
imaging quality, as indicated by irregular illumination or imbalanced
intensity, presents significant obstacles in automating disease screening,
analysis, and diagnosis. Existing approaches for natural image enhancement are
mostly trained with numerous paired images, presenting challenges in data
collection and training costs, all while lacking the ability to generalize
effectively. Here, we introduce a pioneering training-free Diffusion Model for
Universal Medical Image Enhancement, named UniMIE. UniMIE demonstrates its
unsupervised enhancement capabilities across various medical image modalities
without the need for any fine-tuning. It accomplishes this by relying solely on
a single pre-trained model from ImageNet. We conduct a comprehensive evaluation
on 13 imaging modalities and over 15 medical types, demonstrating better
qualities, robustness, and accuracy than other modality-specific and
data-inefficient models. By delivering high-quality enhancement and
corresponding accuracy downstream tasks across a wide range of tasks, UniMIE
exhibits considerable potential to accelerate the advancement of diagnostic
tools and customized treatment plans.
