The inability to acquire clean high-resolution (HR) electron microscopy (EM) images over a large brain tissue volume hampers many neuroscience studies. To address this challenge, we propose a deep-learning-based image super-resolution (SR) approach to computationally reconstruct clean HR 3D-EM with a large field of view (FoV) from noisy low-resolution (LR) acquisition. Our contributions are I) Investigating training with no-clean references for $\ell_2$ and $\ell_1$ loss functions; II) Introducing a novel network architecture, named EMSR, for enhancing the resolution of LR EM images while reducing inherent noise; and, III) Comparing different training strategies including using acquired LR and HR image pairs, i.e., real pairs with no-clean references contaminated with real corruptions, the pairs of synthetic LR and acquired HR, as well as acquired LR and denoised HR pairs. Experiments with nine brain datasets showed that training with real pairs can produce high-quality super-resolved results, demonstrating the feasibility of training with non-clean references for both loss functions. Additionally, comparable results were observed, both visually and numerically, when employing denoised and noisy references for training. Moreover, utilizing the network trained with synthetically generated LR images from HR counterparts proved effective in yielding satisfactory SR results, even in certain cases, outperforming training with real pairs. The proposed SR network was compared quantitatively and qualitatively with several established SR techniques, showcasing either the superiority or competitiveness of the proposed method in mitigating noise while recovering fine details.

翻译：无法在大脑组织大体积上获取干净的、高分辨率（HR）电子显微镜（EM）图像，这阻碍了许多神经科学研究。为应对这一挑战，我们提出了一种基于深度学习的图像超分辨率（SR）方法，通过计算方式从带噪声的低分辨率（LR）采集图像中重建出具有大视场（FoV）的干净高分辨率三维电子显微镜图像。我们的贡献包括：一）研究使用无干净参考图像进行$\ell_2$和$\ell_1$损失函数训练的可行性；二）引入一种名为EMSR的新型网络架构，用于提升低分辨率电子显微镜图像分辨率的同时减少固有噪声；三）比较不同的训练策略，包括使用采集的低分辨率和高分辨率图像对（即带有真实污染的噪声参考图像的真实配对）、由低分辨率合成图像与采集的高分辨率图像配对，以及采集的低分辨率与去噪后的高分辨率图像配对。在九个脑部数据集上的实验表明，使用真实配对训练可产生高质量的超分辨率结果，证明了两种损失函数下使用非干净参考图像进行训练的可行性。此外，在使用去噪参考图像和噪声参考图像进行训练时，在视觉和数值上均观察到可比较的结果。进一步地，利用由高分辨率对应图像合成生成的低分辨率图像训练的网络，能在某些情况下产生令人满意的超分辨率结果，甚至在某些案例中优于使用真实配对的训练效果。本文提出的超分辨率网络与多种现有超分辨率技术进行了定量和定性比较，证明了所提方法在抑制噪声同时恢复精细细节方面具有优势或竞争力。