Structured-illumination microscopy (SIM) offers a twofold resolution enhancement beyond the optical diffraction limit. At present, SIM requires several raw structured-illumination (SI) frames to reconstruct a super-resolution (SR) image, especially the time-consuming reconstruction of speckle SIM, which requires hundreds of SI frames. Considering this, we herein propose an untrained structured-illumination reconstruction neural network (USRNN) with known illumination patterns to reduce the amount of raw data that is required for speckle SIM reconstruction by 20 times and thus improve its temporal resolution. Benefiting from the unsupervised optimizing strategy and CNNs’ structure priors, the high-frequency information is obtained from the network without the requirement of datasets; as a result, a high-fidelity SR image with approximately twofold resolution enhancement can be reconstructed using five frames or less. Experiments on reconstructing non-biological and biological samples demonstrate the high-speed and high-universality capabilities of our method.

中文翻译：

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未经训练的神经网络可实现快速通用的结构照明显微镜

结构照明显微镜 (SIM) 的分辨率提高了一倍，超出了光学衍射极限。目前，SIM需要多个原始结构照明（SI）帧来重建超分辨率（SR）图像，尤其是散斑SIM的重建非常耗时，需要数百个SI帧。考虑到这一点，我们在此提出了一种具有已知照明模式的未经训练的结构化照明重建神经网络（USRNN），以将散斑 SIM 重建所需的原始数据量减少 20 倍，从而提高其时间分辨率。受益于无监督优化策略和CNN结构先验，无需数据集即可从网络中获取高频信息；因此，可以使用五帧或更少的帧来重建具有大约两倍分辨率增强的高保真 SR 图像。重建非生物和生物样本的实验证明了我们的方法的高速和高通用性能力。