Denoising diffusion models for high-resolution microscopy image restoration

Pamela Osuna-Vargas, Maren H. Wehrheim, Lucas Zinz, Johanna Rahm, Ashwin Balakrishnan, Alexandra Kaminer, Mike Heilemann, Matthias Kaschube
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Abstract

Advances in microscopy imaging enable researchers to visualize structures at the nanoscale level thereby unraveling intricate details of biological organization. However, challenges such as image noise, photobleaching of fluorophores, and low tolerability of biological samples to high light doses remain, restricting temporal resolutions and experiment durations. Reduced laser doses enable longer measurements at the cost of lower resolution and increased noise, which hinders accurate downstream analyses. Here we train a denoising diffusion probabilistic model (DDPM) to predict high-resolution images by conditioning the model on low-resolution information. Additionally, the probabilistic aspect of the DDPM allows for repeated generation of images that tend to further increase the signal-to-noise ratio. We show that our model achieves a performance that is better or similar to the previously best-performing methods, across four highly diverse datasets. Importantly, while any of the previous methods show competitive performance for some, but not all datasets, our method consistently achieves high performance across all four data sets, suggesting high generalizability.
用于高分辨率显微图像复原的去噪扩散模型
显微成像技术的进步使研究人员能够在纳米级水平上观察结构,从而揭示生物组织的复杂细节。然而,图像噪声、荧光团的光漂白、生物样本对高光剂量的耐受性低等挑战依然存在,限制了时间分辨率和实验持续时间。降低激光剂量可以延长测量时间,但代价是降低分辨率和增加噪声,从而阻碍了下游分析的准确性。在这里,我们通过在低分辨率信息的基础上训练腺扩散概率模型(DDPM)来预测高分辨率图像。此外,DDPM 的概率方面允许重复生成图像,从而进一步提高信噪比。我们的研究表明,在四个高度多样化的数据集上,我们的模型取得了优于或类似于之前表现最好的方法的性能。重要的是,虽然之前的任何方法都能在某些数据集(而非所有数据集)上显示出具有竞争力的性能,但我们的方法却能在所有四个数据集上持续获得高性能,这表明我们的方法具有很高的通用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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