scholarly journals Analysis of SNR for High-Orbit Target Detected by Ground-Based Photoelectric System

2018 ◽  
Vol 8 (12) ◽  
pp. 2604 ◽  
Author(s):  
Zhiguo Huang ◽  
Rui Huang ◽  
Xiaojun Xue
Keyword(s):  
Low Temperatures ◽  
Background Radiation ◽  
Signal To Noise Ratio ◽  
Short Wave ◽  
Signal To Noise ◽  
Radiation Data ◽  
Simulation Based ◽  
Photoelectric System ◽  
Different Temperatures ◽  
High Orbit

To determine the feasibility of observing high-orbit targets with a large aperture telescope, we created a simulation based on electronics to evaluate the signal-to-noise ratio (SNR) model for an infrared ground-based photoelectric system. Atmosphere transmission and sky background radiation data were obtained using MODTRAN software, then the SNRs of the high-orbit target (HOT) in different temperatures and orbit heights were calculated separately. The results showed that the observation of the HOT in a short band was possible, and the effect of short-wave was excellent at low temperatures. On the basis of this model, some space targets were observed by a K-band photoelectric telescope for verification and had constructive results. Thus, the model can be used as a basis for whether a HOT can be detected.

A New Accessory for Infrared Emission Spectroscopy Measurements

1986 ◽  
Vol 40 (3) ◽  
pp. 401-405 ◽  
Author(s):  
M. Handke ◽  
N. J. Harrick
Keyword(s):  
Background Radiation ◽  
Signal To Noise Ratio ◽  
Focal Length ◽  
Infrared Emission ◽  
Operating Conditions ◽  
Signal To Noise ◽  
Small Areas ◽  
Area Of Interest ◽  
Ellipsoidal Mirror ◽  
Noise Ratio

The principal problem in measurement of emission IR spectra is the low signal-to-noise ratio resulting from the large background radiation relative to sample emission. One method of increasing the signal is to collect the emitted radiation over a very large solid angle using an ellipsoidal mirror. In this method, placing the sample at the short focal length of the ellipsoid both increases the amount of radiation collected for an improved signal-to-noise ratio as well as facilitates sampling of small areas. For locating the area of interest, a microscope is mounted on the emission accessory. The results of testing this emission accessory under different operating conditions such as different samples, emission angles, temperatures, etc., are presented.

Some Considerations about SWIR Gated Camera Performance by Using SNR and Detection Probability Concepts

2016 ◽  
Vol 859 ◽  
pp. 104-109
Author(s):  
Catalin Spulber ◽  
Octavia Borcan
Keyword(s):  
Comparative Analysis ◽  
Image Quality ◽  
Detection Probability ◽  
Signal To Noise Ratio ◽  
Short Wave ◽  
Simulation Software ◽  
Atmospheric Conditions ◽  
Signal To Noise ◽  
Key Indicators

The aim of this paper was to assess the improvement which can be add to the surveillance process which is affected by transmission and atmospheric turbulence using a Short Wave Infrared (SWIR) camera with image gating technology instead of a regular SWIR camera. As criteria for comparative analysis of their performance there were considered two key indicators: Target Detection Probability (Pdet) and image quality, materialized through Signal to Noise Ratio (SNR). The authors have considered a case study which revealed that turbulence and visibility of atmospheric influence less the operability of a SWIR system with image gating than a regular SWIR camera, especially in situations where visibility of the air is very low (less than 1 km). To analyze the behavior of the acquired image in variable atmospheric conditions there were used simulation software image as NVLaserID and SSCamIP.

scholarly journals Simulating shot noise of color underwater images

2020 ◽  
Vol 44 (4) ◽  
pp. 671-679
Author(s):  
D.A. Shepelev ◽  
V.P. Bozhkova ◽  
E.I. Ershov ◽  
D.P. Nikolaev
Keyword(s):  
Signal To Noise Ratio ◽  
Real Data ◽  
Transformation Model ◽  
Image Simulation ◽  
Image Transformation ◽  
Testing Methods ◽  
Signal To Noise ◽  
Underwater Image ◽  
Simulation Based

This paper considers methods for simulating color underwater images based on real terrestrial images. Underwater image simulation is widely used for developing and testing methods for improving underwater images. A large group of existing methods uses the same deterministic image transformation model ignoring the presence of noise in images. The paper demonstrates that this significantly affects the overall quality of underwater images simulation. It is shown both theoretically and numerically that the accuracy of the signal-to-noise ratio of underwater images simulated using a deterministic transformation decreases with increasing distance to the object. To solve this problem, a new model of image transformation for simulating underwater images based on terrestrial images is proposed, which considers the presence of noise in the image and is compatible with all simulating methods from the group under consideration. The paper presents the results of the simulation based on the existing and proposed models, showing that at long distances, the new results are better consistent with real data.

scholarly journals X-Ray Detectors: Present and Future

1996 ◽  
Vol 4 (3) ◽  
pp. 8-9
Author(s):  
Don Chernoff
Keyword(s):  
Trace Elements ◽  
Detection Limit ◽  
Background Radiation ◽  
Signal To Noise Ratio ◽  
Energy Dispersive Spectrometer ◽  
Data Gathering ◽  
X Rays ◽  
Signal To Noise ◽  
X Ray ◽  
Wavelength Dispersive Spectrometer

If you want to collect X-rays on your SEM or TEM you have the choice of using an energy dispersive spectrometer (EDS) or a wavelength dispersive spectrometer (WDS). Which is better? That depends on what you want to do with it.WDS is much more sensitive and has much higher resolution than EDS. Sensitivity means that WDS has a much lower minimum detection limit for trace elements. WDS can be up to several orders of magnitude more sensitive. This does not mean it is more accurate. Accuracy of analysis can be just as good for EDS as with WDS if the analyst takes the same care in sample prep, data gathering, and standardizing. Sensitivity simply means that WDS can detect much smaller amounts of a particular element than EDS. Why? Because the superior resolution provides much better signal to noise ratio than with EDS. Noise, in this case, is mostly contributed by background radiation from the sample. Because of its superior resolution, WDS can also resolve peak overlaps.

Determination of the Best Emulsion for the Microscopy of Crystals

1981 ◽  
Vol 39 ◽  
pp. 32-33
Author(s):  
David A. Grano ◽  
Kenneth H. Downing
Keyword(s):  
Spatial Frequency ◽  
Information Transfer ◽  
Signal To Noise Ratio ◽  
Experimental Situation ◽  
Photographic Emulsion ◽  
Modulation Transfer ◽  
Signal To Noise ◽  
Frequency Space ◽  
Noise Ratio

The retrieval of high-resolution information from images of biological crystals depends, in part, on the use of the correct photographic emulsion. We have been investigating the information transfer properties of twelve emulsions with a view toward 1) characterizing the emulsions by a few, measurable quantities, and 2) identifying the “best” emulsion of those we have studied for use in any given experimental situation. Because our interests lie in the examination of crystalline specimens, we've chosen to evaluate an emulsion's signal-to-noise ratio (SNR) as a function of spatial frequency and use this as our critereon for determining the best emulsion.The signal-to-noise ratio in frequency space depends on several factors. First, the signal depends on the speed of the emulsion and its modulation transfer function (MTF). By procedures outlined in, MTF's have been found for all the emulsions tested and can be fit by an analytic expression 1/(1+(S/S0)2). Figure 1 shows the experimental data and fitted curve for an emulsion with a better than average MTF. A single parameter, the spatial frequency at which the transfer falls to 50% (S0), characterizes this curve.

Experiments in Bright-Field Imaging with Hollow-Cone Illumination

1981 ◽  
Vol 39 ◽  
pp. 226-227
Author(s):  
W. Kunath ◽  
K. Weiss ◽  
E. Zeitler
Keyword(s):  
Signal To Noise Ratio ◽  
Carbon Support ◽  
Electronic System ◽  
Optic Axis ◽  
Hollow Cone ◽  
Signal To Noise ◽  
Bright Field ◽  
Noise Ratio ◽  
Single Field ◽  
Field Imaging

Bright-field images taken with axial illumination show spurious high contrast patterns which obscure details smaller than 15 ° Hollow-cone illumination (HCI), however, reduces this disturbing granulation by statistical superposition and thus improves the signal-to-noise ratio. In this presentation we report on experiments aimed at selecting the proper amount of tilt and defocus for improvement of the signal-to-noise ratio by means of direct observation of the electron images on a TV monitor.Hollow-cone illumination is implemented in our microscope (single field condenser objective, Cs = .5 mm) by an electronic system which rotates the tilted beam about the optic axis. At low rates of revolution (one turn per second or so) a circular motion of the usual granulation in the image of a carbon support film can be observed on the TV monitor. The size of the granular structures and the radius of their orbits depend on both the conical tilt and defocus.

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