Step-Calibrated Diffusion for Biomedical Optical Image Restoration
<|reference_start|>Step-Calibrated Diffusion for Biomedical Optical Image Restoration: High-quality, high-resolution medical imaging is essential for clinical care. Raman-based biomedical optical imaging uses non-ionizing infrared radiation to evaluate human tissues in real time and is used for early cancer detection, brain tumor diagnosis, and intraoperative tissue analysis. Unfortunately, optical imaging is vulnerable to image degradation due to laser scattering and absorption, which can result in diagnostic errors and misguided treatment. Restoration of optical images is a challenging computer vision task because the sources of image degradation are multi-factorial, stochastic, and tissue-dependent, preventing a straightforward method to obtain paired low-quality/high-quality data. Here, we present Restorative Step-Calibrated Diffusion (RSCD), an unpaired image restoration method that views the image restoration problem as completing the finishing steps of a diffusion-based image generation task. RSCD uses a step calibrator model to dynamically determine the severity of image degradation and the number of steps required to complete the reverse diffusion process for image restoration. RSCD outperforms other widely used unpaired image restoration methods on both image quality and perceptual evaluation metrics for restoring optical images. Medical imaging experts consistently prefer images restored using RSCD in blinded comparison experiments and report minimal to no hallucinations. Finally, we show that RSCD improves performance on downstream clinical imaging tasks, including automated brain tumor diagnosis and deep tissue imaging. Our code is available at https://github.com/MLNeurosurg/restorative_step-calibrated_diffusion.<|reference_end|>
@article{lyu2024step-calibrated,
    title={Step-Calibrated Diffusion for Biomedical Optical Image Restoration},
    author={Yiwei Lyu, Sung Jik Cha, Cheng Jiang, Asadur Chowdury, Xinhai Hou,
  Edward Harake, Akhil Kondepudi, Christian Freudiger, Honglak Lee, Todd C.
  Hollon},
    journal={arXiv preprint arXiv:2403.13680},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.13680},
    primaryClass={eess.IV cs.CV}
}