Back Cover: Joint tagging assisted fluctuation nanoscopy enables fast high-density super-resolution imaging (J. Biophotonics 9/2018)

2018 ◽  
Vol 11 (9) ◽  
pp. e201870160
Author(s):  
Zhiping Zeng ◽  
Jing Ma ◽  
Peng Xi ◽  
Canhua Xu
Keyword(s):  
Super Resolution ◽  
High Density ◽  
Back Cover ◽  
Resolution Imaging

scholarly journals 2P295 Localization Algorithm of High-Density Fluorophores, "Wedged Template Matching" for Live Cell Super Resolution Imaging(27. Bioimaging,Poster)

2013 ◽  
Vol 53 (supplement1-2) ◽  
pp. S207
Author(s):  
Shigeo Watanabe ◽  
Yasushi Okada ◽  
Teruo Takahashi ◽  
Keith Bennett ◽  
Tomochika Takeshima
Keyword(s):  
Template Matching ◽  
Super Resolution ◽  
High Density ◽  
Live Cell ◽  
Localization Algorithm ◽  
Resolution Imaging

Joint tagging assisted fluctuation nanoscopy enables fast high-density super-resolution imaging

2018 ◽  
Vol 11 (9) ◽  
pp. e201800020 ◽  
Author(s):  
Zhiping Zeng ◽  
Jing Ma ◽  
Peng Xi ◽  
Canhua Xu
Keyword(s):  
Super Resolution ◽  
High Density ◽  
Resolution Imaging

scholarly journals Sparse deconvolution of high-density super-resolution images

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Siewert Hugelier ◽  
Johan J. de Rooi ◽  
Romain Bernex ◽  
Sam Duwé ◽  
Olivier Devos ◽  
...  
Keyword(s):  
Morphological Changes ◽  
Super Resolution ◽  
Penalized Regression ◽  
High Density ◽  
Wide Field ◽  
Cellular Processes ◽  
Resolution Imaging ◽  
Super Resolution Microscopy ◽  
Simulated Images ◽  
Norm Penalty

Abstract In wide-field super-resolution microscopy, investigating the nanoscale structure of cellular processes, and resolving fast dynamics and morphological changes in cells requires algorithms capable of working with a high-density of emissive fluorophores. Current deconvolution algorithms estimate fluorophore density by using representations of the signal that promote sparsity of the super-resolution images via an L1-norm penalty. This penalty imposes a restriction on the sum of absolute values of the estimates of emitter brightness. By implementing an L0-norm penalty – on the number of fluorophores rather than on their overall brightness – we present a penalized regression approach that can work at high-density and allows fast super-resolution imaging. We validated our approach on simulated images with densities up to 15 emitters per μm-2 and investigated total internal reflection fluorescence (TIRF) data of mitochondria in a HEK293-T cell labeled with DAKAP-Dronpa. We demonstrated super-resolution imaging of the dynamics with a resolution down to 55 nm and a 0.5 s time sampling.

scholarly journals Faster super-resolution imaging of high density molecules via a cascading algorithm based on compressed sensing

2015 ◽  
Vol 23 (14) ◽  
pp. 18563 ◽  
Author(s):  
Yajuan Du ◽  
Hao Zhang ◽  
Mengying Zhao ◽  
Deqing Zou ◽  
Chun Jason Xue
Keyword(s):  
Compressed Sensing ◽  
Super Resolution ◽  
High Density ◽  
Resolution Imaging
Sign in / Sign up

Export Citation Format

Share Document