Optical super-resolution for two unequally bright point sources based on the fractional Hilbert transform

2021 ◽  
Vol 103 (5) ◽  
Author(s):  
Jun Xin ◽  
Yanan Li ◽  
Xiao-Ming Lu
Keyword(s):  
Hilbert Transform ◽  
Super Resolution ◽  
Point Sources ◽  
Bright Point ◽  
Fractional Hilbert Transform

Fractional Hilbert transform

1996 ◽  
Vol 21 (4) ◽  
pp. 281 ◽  
Author(s):  
Adolf W. Lohmann ◽  
David Mendlovic ◽  
Zeev Zalevsky
Keyword(s):  
Hilbert Transform ◽  
Fractional Hilbert Transform

scholarly journals Subtraction of Bright Point Sources from Synthesis Images of the Epoch of Reionization

2011 ◽  
Vol 28 (1) ◽  
pp. 46-57 ◽  
Author(s):  
B. Pindor ◽  
J. S. B. Wyithe ◽  
D. A. Mitchell ◽  
S. M. Ord ◽  
R. B. Wayth ◽  
...  
Keyword(s):  
Active Galactic Nuclei ◽  
Dynamic Range ◽  
Low Frequency ◽  
Neutral Hydrogen ◽  
Galactic Nuclei ◽  
Point Sources ◽  
Field Of View ◽  
Bright Point ◽  
Line Of Sight ◽  
Diffuse Background

AbstractBright point sources associated with extragalactic active galactic nuclei and radio galaxies are an important foreground for low-frequency radio experiments aimed at detecting the redshifted 21-cm emission from neutral hydrogen during the epoch of reionization. The frequency dependence of the synthesized beam implies that the sidelobes of these sources will move across the field of view as a function of observing frequency, hence frustrating line-of-sight foreground subtraction techniques. We describe a method for subtracting these point sources from dirty maps produced by an instrument such as the MWA. This technique combines matched filters with an iterative centroiding scheme to locate and characterize point sources in the presence of a diffuse background. Simulations show that this technique can improve the dynamic range of epoch-of-reionization maps by 2—3 orders of magnitude.

scholarly journals Super-resolution imaging of negative-refractive graded-index photonic crystal flat lens

2021 ◽  
Author(s):  
Binming Liang ◽  
Xiao Huang ◽  
Jihong Zheng
Keyword(s):  
Photonic Crystal ◽  
Imaging System ◽  
Super Resolution ◽  
Point Sources ◽  
Image Resolution ◽  
Multiple Point ◽  
Single Image ◽  
Graded Index ◽  
Resolution Imaging ◽  
Flat Lens

Abstract Photonic crystal (PC) not only breaks through the diffraction limit of traditional lenses but also can realize super-resolution imaging. Improving the resolution is the key task of PC imaging. The main work of this paper is to use a graded-index Photonic crystal (GPC) flat lens to improve the image resolution. An air-hole type two-dimensional (2D) GPC structure based on silicon medium is proposed in this paper. Numerical simulations through RSoft reveal that when the medium in the imaging area is air, the full width at half maximum (FWHM) value of a single image reaches 0.362λ. According to the Rayleigh criterion, the images of two point sources 0.57λ apart can also be distinguished. In the imaging system composed of cedar oil and GPC flat lens, the FWHM value of a single image reaches 0.34λ. In addition, the images of multiple point sources 0.49λ apart can still be distinguished.

A literature review of localization methods and emergence of deep learning in single-molecule localization microscopy

2020 ◽  
Author(s):  
Anish Mukherjee
Keyword(s):  
Deep Learning ◽  
Single Molecule ◽  
Super Resolution ◽  
Point Sources ◽  
Learning Approaches ◽  
Localization Algorithm ◽  
Localization Microscopy ◽  
Trade Offs ◽  
Emitter Localization ◽  
Single Molecule Localization Microscopy

The quality of super-resolution images largely depends on the performance of the emitter localization algorithm used to localize point sources. In this article, an overview of the various techniques which are used to localize point sources in single-molecule localization microscopy are discussed and their performances are compared. This overview can help readers to select a localization technique for their application. Also, an overview is presented about the emergence of deep learning methods that are becoming popular in various stages of single-molecule localization microscopy. The state of the art deep learning approaches are compared to the traditional approaches and the trade-offs of selecting an algorithm for localization are discussed.

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