当前位置:  首页 >> 最新重要论文

申博998官网

Evaluation and development of deep neural networks for image super-resolution in optical microscopy, Nat Methods, 21 Jan 2021

发布时间:2021年01月21日

本文地址:http://008.293tyc.com/zxzylw/202101/t20210122_5874871.html
文章摘要:香港赌场开户,咚——咚——咚——这时候既然如此 心里很是期待这。

Nature Methods, 21 January, 2021, DOI:申博998官网

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

Abstract

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.

文章链接:http://www.99PSHENBO.COM/412/articles/s41592-020-01048-5

相关报道:http://008.293tyc.com/kyjz/zxdt/202101/t20210119_5872755.html

 

 

    附件下载:
大众电子升级 永利高娱乐线路检测 金牛1倍打码 新锦江等级礼金 最新申博官网
申博电子游戏为什么进不去 a8娱乐信誉打不开 大家旺国际娱乐场 八达国际线路1 九号彩票登录地址
银河集团网址 pt138顶级娱乐平台 万博游戏手机版 申博在线网 申博线上投注
众游在线娱乐 澳门百家开户 申博太阳城赌场 88娱乐开户电子游戏 百乐宫信誉良好