scholarly journals Estimation technique of global fringe visibility using in interferometers with adjustable visibility

2020 ◽  
Author(s):  
fu-zhong bai ◽  
Jiayi Chen ◽  
Xiaojuan Gao ◽  
Yongxiang Xu
Keyword(s):  
Theoretical Model ◽  
Fringe Pattern ◽  
Signal To Noise Ratio ◽  
Estimation Technique ◽  
Signal To Noise ◽  
Fringe Visibility ◽  
Low Contrast ◽  
Accuracy Measurement ◽  
Noise Ratio

Abstract In the accuracy measurement of phase from interferometers with adjustable fringe visibility, it needs to estimate the visibility of experimental patterns so as to obtain the interference patterns with the maximum visibility. We develop the Fourier-polar transform and combine the directional projection to estimate the global visibility of carrier fringe pattern. The technique is especially used for low-quality fringe pattern such as low contrast and low (signal to noise ratio) SNR that often appear in the interferometric experiment. An illustrative experiment based on the radial shearing interferometer is given. Results generated from this technique are compared with the derived values from theoretical model, and exemplary agreement between both is demonstrated.

scholarly journals An SNR Estimation Technique Based on Deep Learning

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1139 ◽  
Author(s):  
Kai Yang ◽  
Zhitao Huang ◽  
Xiang Wang ◽  
Fenghua Wang
Keyword(s):  
Deep Learning ◽  
Signal To Noise Ratio ◽  
A Priori ◽  
Intermediate Frequency ◽  
Estimation Technique ◽  
Signal To Noise ◽  
Snr Estimation ◽  
Application Range ◽  
Noise Ratio ◽  
Priori Information

Signal-to-noise ratio (SNR) is a priori information necessary for many signal processing algorithms or techniques. However, there are many problems exsisting in conventional SNR estimation techniques, such as limited application range of modulation types, narrow effective estimation range of signal-to-noise ratio, and poor ability to accommodate non-zero timing offsets and frequency offsets. In this paper, an SNR estimation technique based on deep learning (DL) is proposed, which is a non-data-aid (NDA) technique. Second and forth moment (M2M4) estimator is used as a benchmark, and experimental results show that the performance and robustness of the proposed method are better, and the applied ranges of modulation types is wider. At the same time, the proposed method is not only applicable to the baseband signal and the incoherent signal, but can also estimate the SNR of the intermediate frequency signal.

Elastographic Imaging Using Staggered Strain Estimates

2002 ◽  
Vol 24 (4) ◽  
pp. 229-245 ◽  
Author(s):  
S. Srinivasan ◽  
J. Ophir ◽  
S.K. Alam
Keyword(s):  
Signal To Noise Ratio ◽  
Window Size ◽  
Poor Quality ◽  
Small Distance ◽  
Estimation Technique ◽  
Signal To Noise ◽  
Estimation Techniques ◽  
Strain Estimation ◽  
Noise Ratio ◽  
Data Windows

Conventional techniques in elastography estimate strain as the gradient of the displacement estimates obtained through crosscorrelation of pre- and postcompression rf A-lines. In these techniques, the displacements are estimated over overlapping windows and the strains are estimated as the gradient of the displacement estimates over adjacent windows. The large amount of noise at high window overlaps may result in poor quality elastograms, thus restricting the applicability of conventional strain estimation techniques to low window overlaps, which, in turn, results in a small number of pixels in the image. To overcome this restriction, we propose a multistep strain estimation technique. It computes the first elastogram using nonoverlapped windows. In the next step, the data windows are shifted by a small distance (small fraction of window size) and another elastogram is produced. This is repeated until the cumulative shift equals/exceeds the window size and all the elastograms are staggered to produce the final elastogram. Simulations and experiments were performed using this technique to demonstrate significant improvement in the elastographic signal-to-noise ratio ( SNRe) and the contrast-to-noise ratio ( CNRe) at high window overlaps over conventional strain estimation techniques, without noticeable loss of spatial resolution. This technique might be suitable for reducing the algorithmic noise in the elastograms at high window overlaps.

A superbright X-ray laboratory microsource empowered by a novel restoration algorithm

2012 ◽  
Vol 45 (6) ◽  
pp. 1228-1235 ◽  
Author(s):  
Liberato De Caro ◽  
Davide Altamura ◽  
Fabio Alessio Vittoria ◽  
Gerardina Carbone ◽  
Fen Qiao ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Grazing Incidence ◽  
Building Blocks ◽  
Signal To Noise ◽  
Fringe Visibility ◽  
X Ray ◽  
X Ray Scattering ◽  
Restoration Algorithm ◽  
Noise Ratio ◽  
Ray Scattering

The properties of nanoscale materials vary with the size and shape of the building blocks, which can be measured by (grazing-incidence) small-angle X-ray scattering along with the mutual positions of the nanoparticles. The accuracy in the determination of such parameters is dependent on the signal-to-noise ratio of the X-ray scattering pattern and on the visibility of the interference fringes. Here, a first-generation-synchrotron-class X-ray laboratory microsource was used in combination with a new restoration algorithm to probe nanoscale-assembled superstructures. The proposed algorithm, based on a maximum likelihood approach, allows one to deconvolve the beam-divergence effects from data and to restore, at least partially, missing data cut away by the beam stopper. It is shown that the combination of a superbright X-ray laboratory microsource with the data-restoring method allows a virtual enhancement of the instrument brilliance, improving signal-to-noise ratio and fringe visibility and reaching levels of performance comparable to third-generation synchrotron radiation beamlines.

scholarly journals Proposal of a New Index Based on Signal-to-Noise Ratio for Low-contrast Detectability in Computed Tomographic Imaging

2017 ◽  
Vol 73 (7) ◽  
pp. 537-547
Author(s):  
Junichi Hashimoto ◽  
Shinji Abe ◽  
Yoshiyuki Ishimori ◽  
Masahiko Monma ◽  
Akio Tsumuraya ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Tomographic Imaging ◽  
Signal To Noise ◽  
Low Contrast ◽  
Computed Tomographic ◽  
Noise Ratio ◽  
Computed Tomographic Imaging

Optimization of Local Contrast Factor with Adaptive Brightness Improvement: Impact on Mammogram Image Analysis

2021 ◽  
Vol 11 (8) ◽  
pp. 2153-2166
Author(s):  
Nurshafira Hazim Chan ◽  
Khairunnisa Hasikin ◽  
Nahrizul Adib Kadri ◽  
Mokhzaini Azizan ◽  
Muzammil B. Jusoh
Keyword(s):  
Early Stage ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Local Contrast ◽  
Signal To Noise ◽  
Low Contrast ◽  
Background Removal ◽  
Contrast Factor ◽  
Noise Ratio

Mammography has been known worldwide as the most common imaging modalities utilized for early detection of breast cancer. The mammographic images produced are in greyscale, however they often produced low contrast images, contain artefacts and noise, as well as non-uniform illumination. These limitations can be overcame in the pre-processing stage with the image enhancement process. Therefore, in this research we developed an optimized enhancement framework where the local contrast factor is manipulated to preserve details of the image. This method aims to improve the overall image visibility without altering histogram of the original image, which will affect the segmentation and classification processes. We performed dark background removal in the image histogram at early stage to increase the efficiency of new mean histogram calculation. Then, the histogram is separated into two partitions to allow histogram clipping process to be conducted individually for underexposed and overexposed areas. Consequently, the local contrast factor optimization is conducted to preserve the image details. The results from our proposed method are compared with other methods by the measurement of peak signal-to-noise ratio, structural similarity index, average contrast, and average entropy difference. The results portrayed that our proposed method yield better quality over the others with highest peak signal-to-noise ratio of 32.676. In addition, in terms of qualitative analysis, our proposed method depicted better lesion segmentation with smoother shape of the lesion.

scholarly journals Computer Enhancement of Weak-Beam Images

1973 ◽  
Vol 31 ◽  
pp. 274-275
Author(s):  
H.M. Horgen ◽  
R. E. Villagrana ◽  
D. M. Maher
Keyword(s):  
Signal To Noise Ratio ◽  
Image Contrast ◽  
Signal To Noise ◽  
Low Contrast ◽  
Weak Beam ◽  
Quantitative Image ◽  
Image Width ◽  
Contrast Analysis ◽  
Noise Ratio ◽  
The Individual

In order to perform quantitative image-contrast analysis of low-contrast electron micrographs one must first increase the signal to noise ratio. As an example, consider the weak-beam method of imaging defects by transmission electron microscopy. In this technique an increase in the resolution of closely spaced dislocations is obtained through a narrowing of the individual dislocation image widths. However, this reduction in image width is accompanied by a corresponding decrease in the signal to noise ratio, which in many instances renders quantitative image-contrast analysis impractical. In this note we present an example of the computer image enhancement of a weak-beam micrograph.

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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