Abgaryan Meri, Cui Xinning, Gopan Nandu, Della Maggiora Gabriel, Yakimovich Artur, Sbalzarini Ivo F
Dresden University of Technology, Faculty of Computer Science, 01187, Dresden, Germany.
Max Planck Institute of Molecular Cell Biology and Genetics, 01307, Dresden, Germany.
Small Methods. 2025 Jul;9(7):e2401900. doi: 10.1002/smtd.202401900. Epub 2025 Jun 2.
It is shown that regularizing the signal gradient statistics during training of deep-learning models of super-resolution fluorescence microscopy improves the generated images. Specifically, regularizing the images in the training data set is proposed to have gradient and Laplacian statistics closer to those expected for natural-scene images. The BioSR data set of matched pairs of diffraction-limited and super-resolution images is used to evaluate the proposed regularization in a state-of-the-art generative deep-learning model of super-resolution microscopy, the Conditional Variational Diffusion Model (CVDM). Since the proposed regularization is applied as a preprocessing step to the training data, it can be used in conjunction with any supervised machine-learning model. However, its utility is limited to images for which the prior is appropriate, which in the BioSR data set are the images of filamentous structures. The quality and generalization power of CVDM trained with and without the proposed regularization are compared, showing that the new prior yields images with clearer visual detail and better small-scale structure.
结果表明，在超分辨率荧光显微镜深度学习模型训练过程中对信号梯度统计量进行正则化可改善生成的图像。具体而言，建议对训练数据集中的图像进行正则化，以使梯度和拉普拉斯统计量更接近自然场景图像的预期统计量。使用衍射极限图像和超分辨率图像的匹配对组成的BioSR数据集，在超分辨率显微镜的先进生成深度学习模型——条件变分扩散模型（CVDM）中评估所提出的正则化方法。由于所提出的正则化作为训练数据的预处理步骤应用，因此它可以与任何监督机器学习模型结合使用。然而，其效用仅限于先验合适的图像，在BioSR数据集中，这些图像是丝状结构的图像。比较了使用和不使用所提出的正则化训练的CVDM的质量和泛化能力，结果表明新的先验产生的图像具有更清晰的视觉细节和更好的小尺度结构。
