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.

翻译：高质量、高分辨率的医学成像对于临床诊疗至关重要。基于拉曼效应的生物医学光学成像利用非电离红外辐射实时评估人体组织，被用于癌症早期检测、脑肿瘤诊断及术中组织分析。然而，光学成像易因激光散射和吸收导致图像退化，进而引发诊断错误和治疗偏差。光学图像复原是一项具有挑战性的计算机视觉任务，其退化因素具有多源性、随机性和组织依赖性，难以直接获取成对的低质量/高质量数据。本文提出了一种非配对图像复原方法——复原性步进校准扩散（RSCD），该方法将图像复原问题视为完成基于扩散模型图像生成任务的最后步骤。RSCD采用步进校准器模型动态评估图像退化程度，并确定完成逆向扩散过程所需的步数以实现图像复原。在光学图像复原任务中，RSCD在图像质量和感知评估指标上均优于其他广泛使用的非配对图像复原方法。医学影像专家在盲法对比实验中一致偏好RSCD复原的图像，并报告极少甚至无幻觉现象。最后，我们证明RSCD能够提升下游临床成像任务（包括自动脑肿瘤诊断和深部组织成像）的性能。我们的代码可在 https://github.com/MLNeurosurg/restorative_step-calibrated_diffusion 获取。