We demonstrate residual channel attention networks (RCAN) for the restoration and enhancement of volumetric time-lapse (four-dimensional) fluorescence microscopy data. First we modify RCAN to handle image volumes, showing that our network enables denoising competitive with three other state-of-the-art neural networks. We use RCAN to restore noisy four-dimensional super-resolution data, enabling image capture of over tens of thousands of images (thousands of volumes) without apparent photobleaching. Second, using simulations we show that RCAN enables resolution enhancement equivalent to, or better than, other networks. Third, we exploit RCAN for denoising and resolution improvement in confocal microscopy, enabling ~2.5-fold lateral resolution enhancement using stimulated emission depletion microscopy ground truth. Fourth, we develop methods to improve spatial resolution in structured illumination microscopy using expansion microscopy data as ground truth, achieving improvements of ~1.9-fold laterally and ~3.6-fold axially. Finally, we characterize the limits of denoising and resolution enhancement, suggesting practical benchmarks for evaluation and further enhancement of network performance.

我们展示了用于恢复和增强体积延时（四维）荧光显微镜数据的剩余通道注意网络（RCAN）。首先，我们修改 RCAN 以处理图像体积，表明我们的网络能够与其他三个最先进的神经网络进行去噪竞争。我们使用 RCAN 来恢复嘈杂的四维超分辨率数据，从而能够在没有明显光漂白的情况下捕获超过数万张图像（数千卷）。其次，我们使用模拟表明，RCAN 能够实现与其他网络相当或更好的分辨率增强。第三，我们利用 RCAN 在共聚焦显微镜中进行去噪和分辨率改进，使用受激发射耗尽显微镜地面实况实现约 2.5 倍的横向分辨率增强。第四，我们开发了使用扩展显微镜数据作为基本事实来提高结构化照明显微镜空间分辨率的方法，实现了横向约 1.9 倍和轴向约 3.6 倍的改进。最后，我们描述了去噪和分辨率增强的局限性，为评估和进一步增强网络性能提出了实用的基准。