Step-Calibrated Diffusion for Biomedical Optical Image Restoration

TL;DR

This paper introduces RSCD, a novel unpaired diffusion-based method for restoring degraded biomedical optical images, significantly improving image quality and clinical diagnostic performance without requiring paired training data.

Contribution

RSCD is the first to dynamically calibrate diffusion steps for unpaired optical image restoration, enhancing quality and clinical utility in biomedical imaging.

Findings

RSCD outperforms existing unpaired restoration methods in quality and perceptual metrics.

Clinicians prefer RSCD-restored images with minimal hallucinations.

RSCD improves downstream tasks like tumor diagnosis and deep tissue imaging.

Abstract

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 diffusion-based image restoration method that uses a step calibrator model to dynamically determine 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.