Step-Calibrated Diffusion for Biomedical Optical Image Restoration
arxiv(2024)
摘要
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.
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