Enhanced high-quality super-resolution imaging in air using microsphere lens groups

2020 ◽  
Vol 45 (11) ◽  
pp. 2981
Author(s):  
Hao Luo ◽  
Haibo Yu ◽  
Yangdong Wen ◽  
Tianyao Zhang ◽  
Pan Li ◽  
...  
Keyword(s):  
Super Resolution ◽  
High Quality ◽  
Resolution Imaging

scholarly journals Accelerated FRET-PAINT Microscopy

2018 ◽  
Author(s):  
Jongjin Lee ◽  
Sangjun Park ◽  
Sungchul Hohng
Keyword(s):  
Fluorescence Microscopy ◽  
Super Resolution ◽  
Dissociation Rate ◽  
Dna Probes ◽  
Speed Limit ◽  
High Quality ◽  
Resolution Imaging ◽  
Bleed Through ◽  
Imaging Speed

Recent development of FRET-PAINT microscopy significantly improved the imaging speed of DNA-PAINT, the previously reported super-resolution fluorescence microscopy with no photobleaching problem. Here we try to achieve the ultimate speed limit of FRET-PAINT by optimizing the camera speed, dissociation rate of DNA probes, and bleed-through of the donor signal to the acceptor channel, and further increase the imaging speed of FRET-PAINT by 8-fold. Super-resolution imaging of COS-7 microtubules shows that high-quality 40-nm resolution images can be obtained in just tens of seconds.

Performance and applications of a field-emission gun TEM/STEM

1992 ◽  
Vol 50 (2) ◽  
pp. 942-943
Author(s):  
Judith M. Brock ◽  
Max T. Otten ◽  
Marc. J.C. de Jong
Keyword(s):  
Field Emission ◽  
High Resolution Imaging ◽  
High Quality ◽  
Small Energy ◽  
High Brightness ◽  
Small Probe ◽  
Resolution Imaging ◽  
Convergent Beam ◽  
Beam Patterns ◽  
Beam Diffraction

A Field Emission Gun (FEG) on a TEM/STEM instrument provides a major improvement in performance relative to one equipped with a LaB6 emitter. The improvement is particularly notable for small-probe techniques: EDX and EELS microanalysis, convergent beam diffraction and scanning. The high brightness of the FEG (108 to 109 A/cm2srad), compared with that of LaB6 (∼106), makes it possible to achieve high probe currents (∼1 nA) in probes of about 1 nm, whilst the currents for similar probes with LaB6 are about 100 to 500x lower. Accordingly the small, high-intensity FEG probes make it possible, e.g., to analyse precipitates and monolayer amounts of segregation on grain boundaries in metals or ceramics (Fig. 1); obtain high-quality convergent beam patterns from heavily dislocated materials; reliably detect 1 nm immuno-gold labels in biological specimens; and perform EDX mapping at nm-scale resolution even in difficult specimens like biological tissue.The high brightness and small energy spread of the FEG also bring an advantage in high-resolution imaging by significantly improving both spatial and temporal coherence.

scholarly journals Fast Target Localization Method for FMCW MIMO Radar via VDSR Neural Network

2021 ◽  
Vol 13 (10) ◽  
pp. 1956
Author(s):  
Jingyu Cong ◽  
Xianpeng Wang ◽  
Xiang Lan ◽  
Mengxing Huang ◽  
Liangtian Wan
Keyword(s):  
Neural Network ◽  
Continuous Wave ◽  
Super Resolution ◽  
Mimo Radar ◽  
Target Localization ◽  
Estimation Accuracy ◽  
Low Resolution ◽  
Range Estimation ◽  
Resolution Image ◽  
Resolution Imaging

The traditional frequency-modulated continuous wave (FMCW) multiple-input multiple-output (MIMO) radar two-dimensional (2D) super-resolution (SR) estimation algorithm for target localization has high computational complexity, which runs counter to the increasing demand for real-time radar imaging. In this paper, a fast joint direction-of-arrival (DOA) and range estimation framework for target localization is proposed; it utilizes a very deep super-resolution (VDSR) neural network (NN) framework to accelerate the imaging process while ensuring estimation accuracy. Firstly, we propose a fast low-resolution imaging algorithm based on the Nystrom method. The approximate signal subspace matrix is obtained from partial data, and low-resolution imaging is performed on a low-density grid. Then, the bicubic interpolation algorithm is used to expand the low-resolution image to the desired dimensions. Next, the deep SR network is used to obtain the high-resolution image, and the final joint DOA and range estimation is achieved based on the reconstructed image. Simulations and experiments were carried out to validate the computational efficiency and effectiveness of the proposed framework.

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