Super-resolution structured illumination microscopy (SR-SIM) provides an up to twofold enhanced spatial resolution of fluorescently labeled samples. The reconstruction of high-quality SR-SIM images critically depends on patterned illumination with high modulation contrast. Noisy raw image data (e.g., as a result of low excitation power or low exposure time), result in reconstruction artifacts. Here, we demonstrate deep-learning based SR-SIM image denoising that results in high-quality reconstructed images. A residual encoding–decoding convolutional neural network (RED-Net) was used to successfully denoise computationally reconstructed noisy SR-SIM images. We also demonstrate the end-to-end deep-learning based denoising and reconstruction of raw SIM images into high-resolution SR-SIM images. Both image reconstruction methods prove to be very robust against image reconstruction artifacts and generalize very well across various noise levels. The combination of computational image reconstruction and subsequent denoising via RED-Net shows very robust performance during inference after training even if the microscope settings change.

中文翻译：

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基于深度学习的超分辨率结构照明显微图像降噪与重建

超分辨率结构照明显微镜（SR-SIM）可将荧光标记样品的空间分辨率提高两倍。高质量SR-SIM图像的重建关键取决于具有高调制对比度的图案照明。嘈杂的原始图像数据（例如，由于低激发功率或低曝光时间）导致重建伪像。在这里，我们展示了基于深度学习的SR-SIM图像降噪技术，可产生高质量的重建图像。残差编码-解码卷积神经网络（RED-Net）用于成功地对计算重建的嘈杂SR-SIM图像进行降噪。我们还演示了基于端到端深度学习的原始SIM图像降噪和重构为高分辨率SR-SIM图像。两种图像重建方法都被证明对图像重建伪影非常鲁棒，并且在各种噪声水平下都具有很好的概括性。即使显微镜设置发生变化，计算图像重建和随后通过RED-Net进行的去噪的组合在训练后的推理过程中也显示出非常强大的性能。