Signal-to-noise ratio enhancement on SEM images using a cubic spline interpolation with Savitzky-Golay filters and weighted least squares error

2015 ◽  
Vol 258 (2) ◽  
pp. 140-150 ◽  
Author(s):  
M.A. KIANI ◽  
K.S. SIM ◽  
M.E. NIA ◽  
C.P. TSO
Keyword(s):  
Least Squares ◽  
Spline Interpolation ◽  
Signal To Noise Ratio ◽  
Weighted Least Squares ◽  
Cubic Spline ◽  
Signal To Noise ◽  
Sem Images ◽  
Cubic Spline Interpolation ◽  
Noise Ratio

A Least-Squares Strain Estimator for Elastography

1997 ◽  
Vol 19 (3) ◽  
pp. 195-208 ◽  
Author(s):  
Faouzi Kallel ◽  
Jonathan Ophir
Keyword(s):  
Least Squares ◽  
Spatial Resolution ◽  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Simulated Data ◽  
Signal To Noise ◽  
Noise Ratio ◽  
Gel Phantom

A least-squares strain estimator (LSQSE) for elastography is proposed. It is shown that with such an estimator, the signal-to-noise ratio in an elastogram ( SNRe) is significantly improved. This improvement is illustrated theoretically using a modified strain filter and experimentally using a homogeneous gel phantom. It is demonstrated that the LSQSE results in an increase of the elastographic sensitivity (smallest strain that could be detected), thereby increasing the strain dynamic range. Using simulated data, it is shown that a tradeoff exists between the improvement in SNRe and the reduction of strain contrast and spatial resolution.

scholarly journals Symmetric quadratic tetration interpolation using forward and backward operation combination

2022 ◽  
Vol 12 (2) ◽  
pp. 1893
Author(s):  
Chapkit Charnsamorn ◽  
Suphongsa Khetkeeree
Keyword(s):  
Interpolation Method ◽  
Spline Interpolation ◽  
Signal To Noise Ratio ◽  
Signal Reconstruction ◽  
Sine Wave ◽  
Cubic Spline ◽  
Individual Characteristics ◽  
Second Order ◽  
Cubic Spline Interpolation ◽  
Reconstruction Performance

The existed interpolation method, based on the second-order tetration polynomial, has the asymmetric property. The interpolation results, for each considering region, give individual characteristics. Although the interpolation performance has been better than the conventional methods, the symmetric property for signal interpolation is also necessary. In this paper, we propose the symmetric interpolation formulas derived from the second-order tetration polynomial. The combination of the forward and backward operations was employed to construct two types of the symmetric interpolation. Several resolutions of the fundamental signals were used to evaluate the signal reconstruction performance. The results show that the proposed interpolations can be used to reconstruct the fundamental signal and its peak signal to noise ratio (PSNR) is superior to the conventional interpolation methods, except the cubic spline interpolation for the sine wave signal. However, the visual results show that it has a small difference. Moreover, our proposed interpolations converge to the steady-state faster than the cubic spline interpolation. In addition, the option number increasing will reinforce their sensitivity.

scholarly journals The Effect of Signal-to-Noise Ratio on the Study of Sea Level Trends

2011 ◽  
Vol 24 (5) ◽  
pp. 1396-1408 ◽  
Author(s):  
B. D. Hamlington ◽  
R. R. Leben ◽  
R. S. Nerem ◽  
K.-Y. Kim
Keyword(s):  
Least Squares ◽  
Sea Level ◽  
Least Squares Method ◽  
Secular Trend ◽  
Signal To Noise Ratio ◽  
Empirical Orthogonal Functions ◽  
Signal To Noise ◽  
Orthogonal Functions ◽  
Geophysical Processes ◽  
Noise Ratio

Abstract Extracting secular sea level trends from the background ocean variability is limited by how well one can correct for the time-varying and oscillating signals in the record. Many geophysical processes contribute time-dependent signals to the data, making the sea level trend difficult to detect. In this paper, cyclostationary empirical orthogonal functions (CSEOFs) are used to quantify and improve the signal-to-noise ratio (SNR) between the secular trend and the background variability, obscuring this trend in the altimetric sea level record by identifying and removing signals that are physically interpretable. Over the 16-yr altimetric record the SNR arising from the traditional least squares method for estimating trends can be improved from 4.0% of the ocean having an SNR greater than one to 9.9% when using a more sophisticated statistical method based on CSEOFs. From a standpoint of signal detection, this implies that the secular trend in a greater portion of the ocean can be estimated with a higher degree of confidence. Furthermore, the CSEOF method improves the standard error on the least squares estimates of the secular trend in 97% of the ocean. The convergence of the SNR as the record length is increased is used to estimate the SNR of sea level trends in the near future as more measurements become available from near-global altimetric sampling.

scholarly journals Least Squares Reconstruction of Doppler Radar Spectra for Irregular PRT

2012 ◽  
Vol 29 (12) ◽  
pp. 1744-1756 ◽  
Author(s):  
John Kalogiros
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Signal To Noise Ratio ◽  
Simulated Data ◽  
Irregular Sampling ◽  
Filter Method ◽  
Reconstruction Method ◽  
Signal To Noise ◽  
Mean Velocity ◽  
Noise Ratio

Abstract A least squares method for the reconstruction of Doppler spectra of weather radars with irregular pulse repetition time used to increase the range of unambiguous velocity is presented and evaluated. This method is a robust spectral method that is based on the least squares minimum norm principle and reconstructs both the magnitude and the phase of the discrete Fourier transform of the signal. The phase spectrum is useful in the estimation of the differential phase in dual-polarization radars with staggered sampling schemes, which is a case of irregular sampling. A computationally efficient iterative algorithm for estimating the mean frequency of the signal, which is required for the reconstruction of the spectrum, is described for possible real-time applications. A clutter filter method based on spectral interpolation, which can be applied to echoes with generally nonzero mean velocity, is also described and combined with the spectrum reconstruction method. Using simulated data it is shown that the least squares reconstruction method with or without the presence of clutter gives results with small bias and standard error and can be applied to wide spectra. The application of the method to real X-band radar data with a low signal-to-noise ratio and a high stagger ratio value of ⅚ showed that the least squares method has low sensitivity to the stagger ratio and satisfactorily gives spectral reconstruction for signal-to-noise ratio values as low as 10 dB.

The value of constrained conjugate-gradient least-squares migration in seismic inversion: Application to a fractured-basement play, Texas Panhandle

2017 ◽  
Vol 5 (3) ◽  
pp. SN13-SN23 ◽  
Author(s):  
Thang Ha ◽  
Kurt Marfurt
Keyword(s):  
Least Squares ◽  
Signal To Noise Ratio ◽  
Seismic Inversion ◽  
High Signal ◽  
Radon Transforms ◽  
Signal To Noise ◽  
Data Set ◽  
Texas Panhandle ◽  
Noise Ratio ◽  
Least Squares Migration

Seismic inversion has become almost routine in quantitative 3D seismic interpretation. To ensure the quality of the seismic inversion, the input seismic data need to have a high signal-to-noise ratio. With the current low oil price environment, seismic reprocessing is often preferred over reacquisition to improve data quality. Common filter pairs include forward and inverse [Formula: see text]-[Formula: see text] and Radon transforms. Forward and inverse migrations (i.e., migration and demigration) are a more recently introduced transform pair that, when used together in an iterative workflow, results in a least-squares migration algorithm. Least-squares migration compensates for surface variation in data density and, when combined with a filter applied to prestack migrated images, suppresses the operator and data aliasing. We apply a least-squares migration workflow to a fractured-basement data set from the Texas Panhandle to demonstrate the enhancement in signal-to-noise ratio, the reduction in acquisition footprint and migration artifacts, and the improvement in the P-impedance inversion result.

scholarly journals An Automatic Exposure Method of Plane Array Remote Sensing Image Based on Two-Dimensional Entropy

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3306
Author(s):  
Tan Gao ◽  
Liangliang Zheng ◽  
Wei Xu ◽  
Yongjie Piao ◽  
Rupeng Feng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Dynamic Range ◽  
Imaging System ◽  
Interpolation Method ◽  
Spline Interpolation ◽  
Signal To Noise Ratio ◽  
Two Dimensional ◽  
Signal To Noise ◽  
Exposure Method ◽  
Noise Ratio

The improper setting of exposure time for the space camera will cause serious image quality degradation (overexposure or underexposure) in the imaging process. In order to solve the problem of insufficient utilization of the camera’s dynamic range to obtain high-quality original images, an automatic exposure method for plane array remote sensing images based on two-dimensional entropy is proposed. First, a two-dimensional entropy-based image exposure quality evaluation model is proposed. The two-dimensional entropy matrix of the image is partitioned to distinguish the saturated areas (region of overexposure and underexposure) and the unsaturated areas (region of propitious exposure) from the original image. The ratio of the saturated area is used as an evaluating indicator of image exposure quality, which is more sensitive to the brightness, edges, information volume, and signal-to-noise ratio of the image. Then, the cubic spline interpolation method is applied to fit the exposure quality curve to efficiently improve the camera’s exposure accuracy. A series of experiments have been carried out for different targets in different environments using the existing imaging system to verify the superiority and robustness of the proposed method. Compared with the conventional automatic exposure method, the signal-to-noise ratio of the image obtained by the proposed algorithm is increased by at least 1.6730 dB, and the number of saturated pixels is reduced to at least 2.568%. The method is significant to improve the on-orbit autonomous operating capability and on-orbit application efficiency of space camera.

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