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.

本研究针对显微镜图像中存在的噪声和低分辨率问题，提出了一种基于去噪扩散概率模型（DDPM）的方法，通过低分辨率信息来预测高分辨率图像。实验结果表明，该模型在四个多样化的数据集上表现优于或相当于先前的最佳方法，且在所有数据集上的高性能显示了其良好的通用性。

高分辨率显微镜图像重建的去噪扩散模型