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：获取大范围脑组织体积的清洁高分辨率电子显微镜图像存在困难，这制约了许多神经科学研究。为解决这一挑战，我们提出了一种基于深度学习的图像超分辨率方法，通过计算手段从含噪的低分辨率采集数据中重建具有大视场的清洁高分辨率三维电子显微镜图像。我们的贡献包括：I）研究在无干净参考图像情况下使用ℓ₂和ℓ₁损失函数进行训练；II）引入新型网络架构EMSR，用于提升低分辨率电子显微镜图像分辨率并降低固有噪声；III）比较不同训练策略，包括使用采集的低分辨率与高分辨率图像对（即受真实噪声污染的无干净参考图像对）、合成低分辨率与采集高分辨率图像对，以及采集低分辨率与去噪高分辨率图像对。在九个脑数据集上的实验表明，使用真实图像对进行训练可产生高质量的超分辨率结果，证明了两种损失函数在无干净参考图像训练中的可行性。此外，在训练中使用去噪参考和含噪参考时，视觉和数值结果均表现出可比性。更有趣的是，使用从高分辨率图像合成低分辨率图像训练的网络能有效获得满意的超分辨率结果，在某些情况下甚至优于使用真实图像对的训练。我们将所提出的超分辨率网络与多种成熟超分辨率技术进行定量和定性比较，结果表明该方法在恢复细节时缓解噪声方面具有优越性或竞争力。