scholarly journals Inverse designed extended depth of focus meta-optics for broadband imaging in the visible

Nanophotonics ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Elyas Bayati ◽  
Raphaël Pestourie ◽  
Shane Colburn ◽  
Zin Lin ◽  
Steven G. Johnson ◽  
...  
Keyword(s):  
Numerical Aperture ◽  
Visible Spectrum ◽  
Structural Similarity ◽  
Similarity Metrics ◽  
Post Processing ◽  
Depth Of Focus ◽  
Extended Depth Of Focus ◽  
Point Spread ◽  
Spread Function ◽  
Cubic Function

Abstract We report an inverse-designed, high numerical aperture (∼0.44), extended depth of focus (EDOF) meta-optic, which exhibits a lens-like point spread function (PSF). The EDOF meta-optic maintains a focusing efficiency comparable to that of a hyperboloid metalens throughout its depth of focus. Exploiting the extended depth of focus and computational post processing, we demonstrate broadband imaging across the full visible spectrum using a 1 mm, f/1 meta-optic. Unlike other canonical EDOF meta-optics, characterized by phase masks such as a log-asphere or cubic function, our design exhibits a highly invariant PSF across ∼290 nm optical bandwidth, which leads to significantly improved image quality, as quantified by structural similarity metrics.

scholarly journals Estimation of Ultrasound Echogenicity Map from B-Mode Images Using Convolutional Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4931
Author(s):  
Che-Chou Shen ◽  
Jui-En Yang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Imaging System ◽  
Speckle Pattern ◽  
Image Resolution ◽  
Post Processing ◽  
Point Spread ◽  
Spread Function ◽  
In Silico Simulation ◽  
Tissue Structures

In ultrasound B-mode imaging, speckle noises decrease the accuracy of estimation of tissue echogenicity of imaged targets from the amplitude of the echo signals. In addition, since the granular size of the speckle pattern is affected by the point spread function (PSF) of the imaging system, the resolution of B-mode image remains limited, and the boundaries of tissue structures often become blurred. This study proposed a convolutional neural network (CNN) to remove speckle noises together with improvement of image spatial resolution to reconstruct ultrasound tissue echogenicity map. The CNN model is trained using in silico simulation dataset and tested with experimentally acquired images. Results indicate that the proposed CNN method can effectively eliminate the speckle noises in the background of the B-mode images while retaining the contours and edges of the tissue structures. The contrast and the contrast-to-noise ratio of the reconstructed echogenicity map increased from 0.22/2.72 to 0.33/44.14, and the lateral and axial resolutions also improved from 5.9/2.4 to 2.9/2.0, respectively. Compared with other post-processing filtering methods, the proposed CNN method provides better approximation to the original tissue echogenicity by completely removing speckle noises and improving the image resolution together with the capability for real-time implementation.

scholarly journals Wind-driven halo in high-contrast images

2020 ◽  
Vol 638 ◽  
pp. A98
Author(s):  
F. Cantalloube ◽  
O. J. D. Farley ◽  
J. Milli ◽  
N. Bharmal ◽  
W. Brandner ◽  
...  
Keyword(s):  
Adaptive Optics ◽  
Circumstellar Disks ◽  
Spectral Variation ◽  
Post Processing ◽  
Point Spread ◽  
Very Large Telescope ◽  
Spread Function ◽  
Processing Techniques ◽  
Fundamental Limitation ◽  
Adaptive Optics System

Context. The wind-driven halo is a feature that is observed in images that were delivered by the latest generation of ground-based instruments that are equipped with an extreme adaptive optics system and a coronagraphic device, such as SPHERE at the Very Large Telescope (VLT). This signature appears when the atmospheric turbulence conditions vary faster than the adaptive optics loop can correct for. The wind-driven halo is observed as a radial extension of the point spread function along a distinct direction (this is sometimes referred to as the butterfly pattern). When this is present, it significantly limits the contrast capabilities of the instrument and prevents the extraction of signals at close separation or extended signals such as circumstellar disks. This limitation is consequential because it contaminates the data for a substantial fraction of the time: about 30% of the data produced by the VLT/SPHERE instrument are affected by the wind-driven halo. Aims. This paper reviews the causes of the wind-driven halo and presents a method for analyzing its contribution directly from the scientific images. Its effect on the raw contrast and on the final contrast after post-processing is demonstrated. Methods. We used simulations and on-sky SPHERE data to verify that the parameters extracted with our method can describe the wind-driven halo in the images. We studied the temporal, spatial, and spectral variation of these parameters to point out its deleterious effect on the final contrast. Results. The data-driven analysis we propose provides information to accurately describe the wind-driven halo contribution in the images. This analysis confirms that this is a fundamental limitation of the finally reached contrast performance. Conclusions. With the established procedure, we will analyze a large sample of data delivered by SPHERE in order to propose post-processing techniques that are tailored to removing the wind-driven halo.

scholarly journals Fluorescent Speckle Microscopy

2004 ◽  
Vol 12 (2) ◽  
pp. 3-7
Author(s):  
Stephen W. Carmichael ◽  
Wilma L. Lingle
Keyword(s):  
Point Spread Function ◽  
Numerical Aperture ◽  
Living Cells ◽  
The Other ◽  
Good Thing ◽  
Limited Region ◽  
Dynamic Events ◽  
Point Spread ◽  
Spread Function

Whereas too much of a good thing can be bad, too little of a good tiling can be good. In this case, the accidental dilution of X-rhodamine tubulin injected into living cells resulted in a heterogeneous labeling of micro tubules, rather than visualization of continuous structures. This heterogeneous labeling rendered a speckled image, hence the term fluorescent speckle microscopy (FSM) was introduced. It turns out that FSM yields particularly useful information on dynamic events within living cells, for example the assembly and disassembly of microtubules.There are two recent reviews of FSM by Clare Waterman-Storer and Gaudenz Danuser, one emphasizing the biologic applications of the technique, the other emphasizing the quantitative aspects. For their purposes, they defined a “speckle” as a diffraction-limited region of the image that is significantly brighter than its immediate environment. It can be calculated from the point-spread function (which is determined by the numerical aperture of the lens) that optimally a diffraction-limited region of about 250 nm can be imaged.

scholarly journals IMAGE-FORMING BY MEANS OF AXICON

2021 ◽  
Vol 8 ◽  
pp. 118-125
Author(s):  
Irina G. Palchikova ◽  
Yuliya V. Zhukova ◽  
Evgenii S. Smirnov
Keyword(s):  
Experimental Data ◽  
Classical Concept ◽  
Depth Of Focus ◽  
Point Light Source ◽  
Scalar Diffraction ◽  
Point Spread ◽  
Straight Line ◽  
Spread Function ◽  
Preliminary Experimental Data ◽  
Point Light

A theoretical analysis of the image-forming properties of the conical axicon in the scalar diffraction Kirchhoff-Fresnel approximation within the framework of the theory of linear systems revealed that the classical concept of the point spread function is not applicable to axicon images. In the near diffraction zone, a point light source is imaging by a conical axicon in the form of a segment along a straight line connecting the source and the center of the axicon. Moreover, different annular areas of the axicon form different sections on this segment. When used in tandem with a lens, the axicon can allow to increase the depth of focus. Preliminary experimental data have been obtained, which confirm the theoretical conclusions.

scholarly journals Point spread function modelling for wide-field small-aperture telescopes with a denoising autoencoder

2020 ◽  
Vol 493 (1) ◽  
pp. 651-660 ◽  
Author(s):  
Peng Jia ◽  
Xiyu Li ◽  
Zhengyang Li ◽  
Weinan Wang ◽  
Dongmei Cai
Keyword(s):  
Point Spread Function ◽  
Wide Field ◽  
Calibration Data ◽  
Post Processing ◽  
Denoising Autoencoder ◽  
Small Aperture ◽  
Point Spread ◽  
Spread Function ◽  
Function Modelling ◽  
Star Images

ABSTRACT The point spread function reflects the state of an optical telescope and it is important for the design of data post-processing methods. For wide-field small-aperture telescopes, the point spread function is hard to model because it is affected by many different effects and has strong temporal and spatial variations. In this paper, we propose the use of a denoising autoencoder, a type of deep neural network, to model the point spread function of wide-field small-aperture telescopes. The denoising autoencoder is a point spread function modelling method, based on pure data, which uses calibration data from real observations or numerical simulated results as point spread function templates. According to real observation conditions, different levels of random noise or aberrations are added to point spread function templates, making them realizations of the point spread function (i.e. simulated star images). Then we train the denoising autoencoder with realizations and templates of the point spread function. After training, the denoising autoencoder learns the manifold space of the point spread function and it can map any star images obtained by wide-field small-aperture telescopes directly to its point spread function. This could be used to design data post-processing or optical system alignment methods.

Point spread functions with extended depth of focus

2002 ◽  
Author(s):  
Tony Wilson ◽  
Mark A. A. Neil ◽  
F. Massoumian
Keyword(s):  
Depth Of Focus ◽  
Point Spread Functions ◽  
Extended Depth Of Focus ◽  
Point Spread

scholarly journals Wavefront Modulation for an Elongated Depth of Focus with a Homogeneous Point Spread Function

2010 ◽  
Author(s):  
Daniel Iwaniuk ◽  
Erwin Hack ◽  
Pramod K. Rastogi ◽  
Erwin Hack
Keyword(s):  
Point Spread Function ◽  
Depth Of Focus ◽  
Point Spread ◽  
Spread Function
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