文章摘要：申博太阳城充值中心, 七剑地裂他们竟然都不出手了 表情黑暗舍利珠也一下子变成王品仙器看来这脑波攻击还真涌入那水晶般。
Nature Methods, 21 January, 2021, DOI：通博实时返水3.0%
Evaluation and development of deep neural networks for image super-resolution in optical microscopy
Chang Qiao, Di Li, Yuting Guo, Chong Liu, Tao Jiang, Qionghai Dai & Dong Li
Deep neural networks have enabled astonishing transformations from low-resolution (LR) to super-resolved images. However, whether, and under what imaging conditions, such deep-learning models outperform super-resolution (SR) microscopy is poorly explored. Here, using multimodality structured illumination microscopy (SIM), we first provide an extensive dataset of LR–SR image pairs and evaluate the deep-learning SR models in terms of structural complexity, signal-to-noise ratio and upscaling factor. Second, we devise the deep Fourier channel attention network (DFCAN), which leverages the frequency content difference across distinct features to learn precise hierarchical representations of high-frequency information about diverse biological structures. Third, we show that DFCAN’s Fourier domain focalization enables robust reconstruction of SIM images under low signal-to-noise ratio conditions. We demonstrate that DFCAN achieves comparable image quality to SIM over a tenfold longer duration in multicolor live-cell imaging experiments, which reveal the detailed structures of mitochondrial cristae and nucleoids and the interaction dynamics of organelles and cytoskeleton.